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SYMPHONY 5.2.3 User`s Manual 1 - Coin-OR

1. OsiSymGenerateCglGomoryCuts generate_cgl_gomory_cuts boolean OsiSymGenerateCglKnapsackCuts generate_cg _knapsack_cuts boolean OsiSymGenerateCglOddHoleCuts generate_cgl oddhole_cuts boolean OsiSymGenerateCglProbingCuts generate_cgl probing cuts boolean OsiSymGenerateCglCliqueCuts generate_cgl_clique_cuts boolean OsiSymGenerateCglFlowAndCoverCuts generate_cgl flow_and_cover_cuts boolean OsiSymGenerateCglRoundingCuts generate_cgl_rounding_cuts boolean OsiSymGenerateCglLiftAndProjectCuts generate_cgl_lift_and_project_cuts boolean OsiSymKeepWarmStart keep_warm_start boolean OsiSymTrim Warm Tree trim_warm_tree boolean OsiSymDoReducedCostFixing do_reduced_cost_fixing boolean OsiSymMCFindSupportedSolutions mc_find_supported_solutions boolean OsiSymSensitivity Analysis sensitivity_analysis boolean OsiSymRandomSeed random_seed user defined OsiSymDivingStrategy diving strategy BEST_ESTIMATE COMP BESTK COMP_BEST_K_GAP OsiSymDivingK diving_k user defined OsiSymDivingThreshold diving_threshold user defined OsiSymGranularity granularity user defined OsiSymTimeLimit time_limit user defined OsiSymGapLimit gap_limit user defined OsiObjOffset user defined OsiProbName problem_name user defined However as it is seen only some of the C interface parameters have their
2. This routine is used to set a priori upper lower bound to the problem Arguments sym_environment env INOUT Pointer to the SYMPHONY environment double double IN The value of the priori upper for minimization or lower for maximization bound Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 133 gt sym_set_continuous int sym_set_continuous sym_environment env int index Description This routine is used to set the type of a variable to be continuous Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN The index of the variable to be modified Note that it has to be at most the number of columns Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 134 gt sym_set_integer int sym_set_continuous sym_environment env int index Description This routine is used to set the type of a variable to be integer Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN The index of the variable to be modified Note that it has to be at most the number of columns Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 135 gt sym_set_col_names int sym_set_col_names sym_enviro
3. The name of the LP CG and CP mod ule binaries Note when running in parallel using PVM these executables or links to them must reside in the PVM_ROOT bin PVM_ARCH directory Also be sure to note that the exe cutable names may have extensions that depend on the configuration of the modules but the defaults will always be set to the name that the makefile produces lp_debug cg_debug cp_debug boolean all FALSE Whether the modules should be started under a debugger or not 223 max_active_nodes integer 1 The maximum number of active search tree nodes equal to the number of LP and CG tandems to be started up max_cp_num integer 0 The maximum number of cut pools to be used 1p_mach_num cg_mach_num cp_mach_num integers all 0 The number of processors in the virtual machine to run LP CG CP processes If this value is O then the processes will be assigned to processors in round robin order Otherwise the next xx_mach_num lines describe the processors where the LP CG CP modules must run The keyword value pairs on these lines must be TM_xx machine and the name or IP address of a processor the processor names need not be distinct In this case the actual processes are assigned in a round robin fashion to the processors on this list This feature is useful if a specific software package is needed for some module but that software is not licensed for every node of the virtual machine or if a certain
4. verbosity 3 node_selection_rule 3 time_limit 100 24 Then type param_file to be prompted to read in the parameter file SYMPHONY Set param_file Name of the parameter file my_param Setting verbosity to 3 Setting node_selection_rule to 3 Setting time_limit to 100 SYMPHONY Set At this point you can return to the main menu by typing back load an instance and solve it with updated run time parameters 3 1 5 Display Menu The Display submenu is used to print out results and statistics of the solution process after a solve call To enter this submenu and see available options type display and then help or SYMPHONY display Please type help to see the display options SYMPHONY Display help List of display options solution display the column values obj display the objective value stats display the statistics parameter display the value of a parameter back leave this menu quit exit leave the optimizer SYMPHONY Display Clearly in order to display column solutions and the optimal solution value you need to type solution and then obj SYMPHONY Display solution Optimal Solution found PARA A A A A A A A A A A A EAE EEF Nonzero column names and values in the solution PARA A A A A RA A A A A A A A A A y COLO0002 3 000 COLO0006 1 000 25 SYMPHONY Display obj Objective Value 40 000000 SYMPHONY WDisplay You can also display the values of SY MPHONY s run time param
5. Although there are default options for many of the operations the user can also assert control over the behavior of the algorithm by overriding the default methods or by parameter setting Although we have described our approach as being object oriented we would like to point out that SYMPHONY is implemented in C not C To avoid inefficiencies and enhance the modu larity of the code allowing for easy parallelization we used a more function oriented approach for the implementation of certain aspects of the framework For instance methods used for com 38 municating data between modules are not naturally object oriented because the type of data being communicated is usually not known by the message passing interface It is also common that efficiency considerations require that a particular method be performed on a whole set of objects at once rather than on just a single object Simply invoking the same method sequentially on each of the members of the set can be extremely inefficient In these cases it is far better to define a method which operates on the whole set at once In order to overcome these problems we have also defined a set of interface functions which are associated with the computational modules These function is described in detail in Section 6 3 4 3 2 Data Structures and Storage Both the memory required to store the search tree and the time required to process a node are largely dependent on the n
6. 6 Reference 71 61 Callable Library CG APL co 2 64 eee hee ee ee ee ae bY 71 6 1 1 Primary Interface Functions o 2686 84 se Rd eRe ee a es 72 6 1 2 Parameter Query and Modification 0 000 eee eee 87 6 1 3 Solver Status Query Functions lt s ee a sesse sbor eei ee 94 6 14 Data Query Functions s s eo a magosa soka pa deag E a e 100 6 1 5 Data Modification Pumections 2 42 i4 62 6583 e g R e e 124 6 1 6 Warm Starting Functions o e s sates 2020402029402 e a 141 6 1 7 Sensitivity Analysis Functions ooo 0000 ee eee eee 147 6 2 Callable Library C API 0200000002 eee eee 151 Ge User Callback APT oca a a ee a ee ee RA aa 154 6 3 1 Master module callbacks e e 2 154 6 3 2 LP module callbacks sso 2s eee airi Ka ee aaa ee a a ee 170 6 3 3 Cut generator module callbacks nonoa a 204 vii 6 3 4 Cut pool module callbacks 246 45 bee be RR Re Ee 210 6 3 5 Draw graph module callbacks lt sa ooe s c toroa tares ee 216 a Eun time Patoticiers on ke es ban ee ee ee a ee a Ee we 220 G41 Global parameters s sa sosa oa be Se a Re ee ee 220 64 2 Master module parameter 24 24 sace ek p k e ee es 220 6 43 Draw Graph parameters 42 25 4s e RR Re ee 223 6 4 4 Tree Manager parameters e 223 e o a oae a ae ec wows pete Behe Se a ee es ie Re gee 227 6 4 6 Cut Generator Parameters e o 233 Oa Cut Pool Parameter s es
7. INT An integer indicating the value of the parameter 91 gt sym_get_dbl_param double sym_get_int_param sym_environment env char key Description This routine is used to get the value of a double type parameter Arguments sym_environment env INOUT Pointer to the SYMPHONY environment char key IN The name of the parameter Return values DOUBLE A double indicating the value of the parameter 92 gt sym_get_str_param char sym_get_int_param sym_environment env char key Description This routine is used to get the value of a string type parameter Arguments sym_environment env INOUT Pointer to the SYMPHONY environment char key IN The name of the parameter Return values CHAR A character array indicating the value of the parameter 93 6 1 3 Solver Status Query Functions gt sym_get_status int sym_get_status sym_environment env Description This post solution query routine is used to learn the termination status of the solution procedure Arguments sym_environment env IN Pointer to the SYMPHONY environment Return values ERROR__USER TM_OPTIMAL_SOLUTION_FOUND TM_TIME_LIMIT_EXCEEDED TM_NODE_LIMIT_EXCEEDED TM_TARGET_GAP_ACHIEVED TM_FOUND_FIRST_FEASIBLE TM_ERROR__NO_BRANCHING_CANDIDATE TM_ERROR__ILLEGAL_RETURN_CODE TM_ERROR__NUMERICAL_INSTABILITY TM_ERROR__COMM_ERROR TM_ERROR__USER 94 Error User error detected in one of user_send_lp_data user_send_cg_da
8. This function is invoked only in the case parallel computation and only if in the user_send_lp_solution function of the LP module the user opted for packing the current LP solution in a custom format Here she must receive the data she sent there Arguments void user IN Pointer to the user defined data structure Return values USER_ERROR Cuts are not checked for this LP solution USER_SUCCESS The user function executed properly USER DEFAULT SYMPHONY s default format should be used Invoked from Whenever an LP solution is received Note SYMPHONY automatically unpacks the level index and iteration number correspond ing to the current LP solution within the current search tree node 211 gt user_prepare_to_check_cuts int user_prepare_to_check_cuts void user int varnum int indices double values Description This function is invoked after an LP solution is received but before any cuts are tested Here the user can build up data structures e g a graph representation of the solution that can make the testing of cuts easier in the user_check_cuts function Arguments void user IN Pointer to the user defined data structure int varnum IN The number of nonzero fractional variables described in indices and values int indices IN The user indices of the nonzero fractional variables double values IN The nonzero fractional values Return values USER_ERROR Cuts are not checked for this LP solution USER_SUCCE
9. This means multiple debugging windows which have to be closed and restarted each time the application is re run For this reason it is highly recommended to develop code that can be compiled serially even if you eventually intend to run in a fully distributed environment This does make the coding marginally more complex but believe me it s worth the effort The vast majority of your code will be the same for either case Make sure to use the configuration flag to enable debug while configuring see Section 2 2 2 2 5 6 2 Debugging with PVM If you wish to venture into debugging your distributed application then you simply need to set the parameter _debug where is the name of the module you wish to debug to 1 in the parameter file This will tell PVM to spawn the particular process or processes in question under a debugger What PVM actually does in this case is to launch the script PVM_ROOT 1ib debugger You will undoubtedly want to modify this script to launch your preferred debugger in the manner you deem fit If you have trouble with this please send e mail to the list serve see Section 1 6 It s a little tricky to debug interacting parallel processes The main difficulty is in that the order of operations is difficult to control Random interactions can occur when processes run in parallel due to varying system loads process priorities etc Therefore it may not always be possible to duplicate errors To force runs that
10. enable tests enable tm tests enable trace path enable cut check enable statistics enable pseudo costs enable draw graph disable generic cut generation compile in the sensitivity analysis features process only the root node compile in the fractional branching option perform additional sanity checks for debugging purposes perform more tests additional debugging options additional debugging options additional statistics enable some experimental pseudo cost branching tools enable IGD graph drawing application with XXX incdir with XXX 1ib with lp solver 1psolver with application specify the directory with the header files for the XXX package where XXX is one of LP solver packages cplex glpk osl soplex xpress specify the flags to link with the library XXX package where XXX is one of LP solver packages cplex glpk osl soplex xpress specify the LP solver in small letters default Ipsolver clp compile the application library enable openmp with pvm without cg without cp without lp without tm compile in OpenMP features compile in parallel architecture assuming that pvm is installed and the variable PVM_ROOT is defined compile without cut generator module compile without cut pool module compile without LP solver module compile without tree manager module Figure 2 1 A list of useful configuration options 20 Chapter 3 Using SYMPHONY 3 1 Using SY
11. num_fixed Description Logical fixing is modifying the stati of variables based on logical implications derived from problem specific information In this function the user can modify the status of any variable Valid stati are NOT_FIXED TEMP_FIXED_TO_LB PERM_FIXED_TO_LB TEMP_FIXED_TO_UB and PERM_FIXED_TO_UB Be forewarned that fallaciously fixing a vari able in this function can cause the algorithm to terminate improperly Generally a variable can only be fixed permanently if the matrix is full at the time of the fixing i e all variables that are not fixed are in the matrix There are no default options Arguments void user IN Pointer to the user defined LP data structure int varnum IN The number of variables currently in the LP relaxation The length of the vars and x arrays var_desc vars IN The variables currently in the LP relaxation double x IN Values of the above variables char status INOUT Stati of variables currently in the LP relaxation int num fixed OUT Number of fixed variables Return values USER_ERROR Error Ignored by SYMPHONY USER_SUCCESS User changed the stati of the variables she wanted USER_DEFAULT No logical fixing rules are implemented Wrapper invoked from fix_variables after doing reduced cost fixing but only when a specified number of variables have been fixed by reduced cost see LP parameter settings 196 gt user_generate_column int user_generate_column void user in
12. set SYMPHONY Set verbosity 3 SYMPHONY set verbosity 3 SYMPHONY set param_file my_param 3 2 Using SYMPHONY from the Command Line For batch processing and scripting SYMPHONY can also be called from the command line from a terminal in any operating system note that in the Windows terminal the path separator is rather than When called from the command line a number of command line switches can be invoked to specify the file to be read and solved as well as set parameters Note that the switches are Unix style even in Windows At a minimum one must specify the name of the file to be read and solved The following is the calling sequence to load in an instance file in MPS format and solve it symphony F sample mps To read and solve a model in LP format the command would be symphony L sample lp To read and solve a GMPL model and associated data file the command would be symphony F sample mod D sample dat In addition to specifying the name of the instance file most of the common parameters can also be set on the command line by adding various switches Calling SYMPHONY with just the argument h will list all the options To set parameters that cannot be set on the command line or to save parameter setting it is possible to use a parameter file in which a group of parameters can be set To invoke SYMPHONY with a parameter file type symphony f filename where filename is the name of the parameter file The format of
13. OUT Pointer to the array of candidates gener ated see description below int action OUT What to do Must be one of the four above described values unless the return code is USER_DEFAULT Return values USER_ERROR Error DEFAULT is used USER_SUCCESS The user filled out action and possibly cand_num and candidates USER DEFAULT Action taken is controlled by the parameter shall_we_branch default which is initially USER_BRANCH_IF_MUST unless overridden by the user Notes e The user has to allocate the pointer array for the candidates and place the pointer for the array into candidates if candidates are returned e Candidates of type VIOLATED_SLACK are always added to the LP relaxation regardless of what action is chosen and whether branching will be carried out or not 183 e Also note that the user can change his mind in user_select_candidates and not branch after all even if she chose to branch in this function A possible scenario cut_num is zero when this function is invoked and the user asks for USER__BRANCH_IF_MUST without checking the slack constraints and slack cuts After ward no columns are generated no dual infeasible variables found and thus SYM PHONY decides branching is called for and invokes user_select_candidates However in that function the user checks the slack cuts finds that some are vio lated cancels the branching request and adds the violated cuts to the relaxation instead Warning The c
14. SYMPHONY prints out its own branching information Wrapper invoked from branch after the best candidate has been selected pre solved and the action is decided on for the children 190 gt user_add_to_desc int user_add_to_desc void user int desc_size char desc Description Before a node description is sent to the TM the user can provide a pointer to a data structure that will be appended to the description for later use by the user in reconstruction of the node This information must be placed into desc Its size should be returned in desc_size There is only one default option the description to be added is considered to be of zero length i e there is no additional description Arguments void user IN Pointer to the user defined LP data structure int desc_size OUT The size of the additional information the length of desc in bytes char desc OUT Pointer to the additional information space must be allo cated by the user Return values USER_ERROR Error DEFAULT is used USER_SUCCESS User filled out desc_size and desc USER_DEFAULT No description is appended Wrapper invoked from create_explicit_node_desc before a node is sent to the tree manager 191 gt user_same_cuts int user_same_cuts void user cut_data cut1 cut_data cut2 int same_cuts Description Determine whether the two cuts are comparable the normals of the half spaces corre sponding to the cuts point in the same
15. The Cut Generation Module 2 2 do somad aua 53 4 4 5 The Cut Management Module e e 53 45 Parallelizing BCP 2 2 bee we aoad ee eA ee bee we he eae es 54 45 1 Parallel Conligurations lt c sa 6440 45444 ope eee Re eee Pee 55 4 5 2 Inter process Communication lt se sse vepar eee ee ee ee 55 Oye li o o eea at Sk he Soe Edged ae wee Oe ws Go ee eG 56 5 Developing Custom Applications 57 5 1 Navigating the Source Code gt s ose s sacma ee 57 vi 52 Building an Application oc se s 520092 mera a a ee Ea 58 Peel Wn e ia aa gi kag a amp dae ode es te GeO kn oo er ee 58 Gee Microso visual t te ok ce he ee A ee ee ee A ah 58 Gua Metas e Callbacks ces aca ee a Re eee eh ewe ee 59 Sa Data SUCHT o ui a e ee a a ee ee e 60 55 Parallel Implementation soc 2 24 ose pe a a ee ae 61 5 5 1 Distributed memory Architectures o o e e 61 5 5 2 Shared memory Architectures 2 054 8b bee ee kee 61 9 6 Debugging Your Application s so e gore me rss kee RE Ree RE 62 HO The First Rule ca ek ee bee a eR Se A ee ee eee 62 5 6 2 Debugging with PVM 2 2 4 22545 un ee Ree e a aaae 62 5 6 3 Checking the Validity of Cuts and Tracing the Optimal Path 63 5 6 4 Using the Interactive Graph Drawing Software 63 6 5 Other Debugging Techrigu s osa miisi m 244 soe ee oe ee Be es 64 5 7 Case Study Implementing a Matching Solver 002 000 005 64
16. The switch H is reserved for help and calls the user s usage subroutine see user_usage 6 3 1 If the user returns USER_DEFAULT then SYMPHONY will look for the command line switches F to specify the file name for reading in the model from either an MPS or a GMPL AMPL file or L to specify the file name for reading in the model from an LP format file The D command line switch is used to specify an additional data file for GMPL AMPL models If the D option is not present SYMPHONY assumes the file is an MPS file Arguments void user IN Pointer to the user defined data structure char filename IN The name of the parameter file Return values USER_ERROR Error SYMPHONY stops USER_SUCCESS User parameters were read successfully USER DEFAULT SYMPHONY will read in the problem instance from either an MPS LP or GMPL AMPL file The command line switches F L and D as appropriate will be used to specify the model file 156 gt user_io int user_io void user Description Here the user can read in an instance in a custom format and set up appropriate data structures If the user wants to use the default parsers to read either an MPS file or a GMPL AMPL file then the return value USER DEFAULT should be specified see user_readparams 6 3 1 for the command line switches to use to specify this behavior Arguments void user IN Pointer to the user defined data structure Return values USER_ERROR Error SYMPHONY stop
17. To start the computation from a given warm start when the problem data has not been modified the tree manager simply traverses the tree and adds those nodes marked as candidates for processing to the node queue Once the queue has been reformed the algorithm is then able to pick up exactly where it left off Code for using the resolve command was shown in Figure 3 2 The situation is more challenging if the user modifies problem data in between calls to the solver We address this situation next Modifying Problem Data If the user modifies problem data in between calls to the solver SYMPHONY must make corresponding modifications to the leaf nodes of the current search tree to allow execution of the algorithm to continue In principle any change to the original data that does not invalidate the subproblem warm start data i e the basis information for the LP relaxation can be accommodated Currently SYMPHONY can only handle modifications to the rim vectors 46 of the original MILP Methods for handling other modifications such as the addition of columns or the modification of the constraint matrix itself will be added in the future To initialize the algorithm each leaf node regardless of its status after termination of the previous solve call must be inserted into the queue of candidate nodes and reprocessed with the changed rim vectors After this reprocessing the computation can continue as usual Optionally the user can trim the tre
18. are grouped into five independent computational modules This modular implementation not only facilitates code maintenance but also allows easy and highly configurable parallelization Depending on the computational setting the modules can be compiled as either 1 a single sequential code 2 a multi threaded shared memory parallel code or 3 separate processes running in distributed fashion over a network The modules pass data to each other either through shared memory in the case of sequential computation or shared memory parallelism or through a message passing protocol defined in a separate communications API in the case of distributed execution an schematic overview of the modules is presented in Figure 4 4 In the remainder of the section we describe the modularization scheme and the implementation of each module in a sequential environment 4 3 3 1 The Master Module The master module includes functions that perform problem initialization and I O This module is the only persistent module and stores all static problem data The other modules are created only during a solve call and destroyed afterward All calls to the API are processed through the master module These functions of the master module implement the following tasks e Initialize the environment e Set and maintain parameter values e Read and store static problem data for instance to be solved e Compute an initial upper bound using heuristics 41 The Modules o
19. been purged from the LP can be made active again if they later become violated The number of variables columns in the relaxation is controlled through reduced cost fixing and dynamic column generation Periodically each active variable is priced to see if it can be fixed by reduced cost That is the LP reduced cost is examined in an effort to determine whether fixing that variable at one of its bounds would remove improving solutions if not the variable is fixed and removed from consideration If the matrix is full at the time of the fixing meaning that all unfixed variables are active then the fixing is permanent for that subtree Otherwise it is temporary and only remains in force until the next time that columns are dynamically generated Because SYMPHONY was originally designed for combinatorial problems with relatively small numbers of variables techniques for performing dynamic column generation are somewhat unre fined Currently variables are priced out sequentially by index which can be costly To improve the process of pricing variables we plan to increase the symmetry between our methods for handling variables and those for handling cuts This includes 1 allowing user defined abstract represen tations for variables 2 allowing the use of variable generators analogous to cut generators 3 implementing both global and local pools for variables 4 implementing heuristics that help determine the order in which the inde
20. by hand for errors Many times CPLEX gives much more explicit error messages interactively than through the callable library The form of the function is void write_mps LPdata lp_data char fname where fname is the name of the file to be written If SYMPHONY is forced to abandon solution of an LP because the LP solver returns an error code the current LP relaxation is automatically written to the file matrix bc_index iter_num mps where bc_index is the index of the current subproblem and iter_num is the current iteration number The write_mps function can be called using breakpoint code to examine the status of the matrix at any point during execution Logging is another useful feature Logging the state of the search tree can help isolate some problems more easily See Section 6 4 4 for the appropriate parameter settings to use logging 5 7 Case Study Implementing a Matching Solver This section was contributed by Michael Trick a few years ago and is a walkthrough of the steps for developing a very simple application using SYMPHONY Rather than presenting the code in its final version we will go through the steps that a user would go through Note that some of the code is lifted from the vehicle routing application This code is designed to be a sequential code The MATCH application discussed here is part of the SYMPHONY distribution and the source code can be found in the SYMPHONY Applications MATCH directory The goal is to cr
21. can be used for debugging or if the user wants to save the preprocessed instance prep time limit integer 50 Determines the maximum time in seconds that can be spent in preprocessing 6 4 3 Draw Graph parameters source_path string The directory where the DG tcl tk scripts reside echo_commands boolean FALSE Whether to echo the tcl tk commands on the screen or not canvas_width canvas _height integers 1000 700 The default width and height of the drawing canvas in pixels viewable_width viewable height integers 600 400 The default viewable width and height of the drawing canvas in pixels interactive mode integer TRUE Whether it is allowable to change things interactively on the canvas or not node_radius integer 8 The default radius of a displayed graph node disp_nodelabels disp_nodeweights disp_edgeweights integers all TRUE Whether to display node labels node weights and edge weights or not nodelabel_font nodeweight_font edgeweight_font strings all adobe helvetica The default character font for displaying node labels node weights and edge weights node dash edge dash strings both empty string The dash pattern of the circles drawn around dashed nodes and that of dashed edges 6 4 4 Tree Manager parameters TM_verbosity integer 0 The verbosity of the TM module lp_exe cg_exe cp_exe strings Ip cg cp
22. csm ornl gov pvm It should compile and install without problems on most architectures You will have to make a few modifications to your cshrc file such as defining the PVM_ROOT environment variable but this is all explained clearly in the PVM documentation Note that all executables or at least a link to them must reside in the PVM_ROOT bin PVM_ARCH directory in order for parallel processes to be spawned correctly The environment variable PVM_ARCH is set in your cshrc file and should contain a string representing the current architecture type To run a parallel application you must first start up the daemon on each of the machines you plan to use in the computation How to do this is also explained in the PVM documentation To configure for a parallel build with the default parallel configuration invoke the configuration script as follows configure with pvm Note that there are a number of different parallel configurations see Chapter 4 3 3 for an overview of SYMPHONY s parallel modules The default configuration is to build two parallel modules the first consisting of the master tree management and cut management modules while the second consisting of the node processing and cut generation modules To build in another configuration there are four configure flags that control which modules run as separate executables and which are called directly in serial fashion The variables are as follows with cg If set then the
23. cut generator function will be called directly from the LP in serial fashion instead of running as a separate executable This is desirable if cut generation is quick and running it in parallel is not worth the price of the communication overhead with cp If set then the cut pool s will be maintained as a data structure auxiliary to the tree manager 13 with 1p If set then the LP functions will be called directly from the tree manager When running the distributed version this necessarily implies that there will only be one active subproblem at a time and hence the code will essentially be running serially In the shared memory version however the tree manager will be threaded in order to execute subproblems in parallel with tm If set then the tree will be managed directly from the master process This is only recommended if a single executable is desired i e the three other variables are also set to true A single executable is extremely useful for debugging purposes These variables can be set in virtually any combination though some don t really make much sense Note that in a few user functions that involve process communication there will be different versions for serial and parallel computation This is accomplished through the use of ttifdef statements in the source code This is well documented in the function descriptions and the in the source files containing the function stubs Once configured follow the b
24. cuts allowed to be stored When this max is reached cuts are forcibly purged starting with duplicates and then those indicated by the parameter delete_which see below until the list is below the allowable size min to delete integer 1000 Indicates the number of cuts required to be deleted when the pool reaches it s maximum size touches_until_deletion integer 10 When using the number of touches a cut has as a mea sure of its quality this parameter indicates the number of touches a cut can have before being deleted from the pool The number of touches is the number of times in a row that a cut has been checked without being found to be violated It is a measure of a cut s relevance or effectiveness delete which integer DELETE BY_TOUCHES 2 Indicates which cuts to delete when purging the pool DELETE_BY_TOUCHES indicates that cuts whose number of touches is above the threshold see touches until deletion above should be purged if the pool gets too large DELETE_BY_QUALITY 1 indicates that a user defined measure of quality should be used see the function user_check_cuts in Section6 3 4 check which integer CHECK_ALL_CUTS 0 Indicates which cuts should be checked for vi olation The choices are to check all cuts CHECK_ALL_CUTS 0 only those that have number of touches below the threshold CHECK_TOUCHES 2 only those that were gen erated at a level higher in the tree than the current one CHECK_LEVEL 1 o
25. double value IN New lower bound of the row Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 128 gt sym_set_row_upper double sym_set_row_upper sym_environment env int index double value Description This routine is used to set the upper bound of a row Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN Index of the row Note that it has to be at most the number of rows double value IN New upper bound of the row Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 129 gt sym_set_row_ type int sym_set_row_type sym_environment env int index char rowsense double rowrhs double rowrng Description This routine is used to set the characteristics of a row Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN Index of the row Note that it has to be at most the number of rows char rowsense IN New sense of the row double rowrhs IN New value of the right hand side of the row double rowrng IN New value of the row range Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 130 gt sym_set_obj_sense int sym_set_obj_sense sym_environment env int sense Descript
26. file sample Type of the file mps ampl gmpl mps Coin00011 At line 1 NAME SAMPLE Coin00011 At line 2 ROWS Coin00011 At line 6 COLUMNS Coin00011 At line 25 RHS Coin00011 At line 28 BOUNDS Coin00011 At line 34 ENDATA Coin00021 Problem SAMPLE has 2 rows 6 columns and 10 elements SYMPHONY Tf the input is in AMPL GMPL format you will also be prompted to read in a data file note again that in order to enable GMPL AMPL reader you have to install GLPK see Section 2 2 2 3 SYMPHONY load Name of the file sample mod Name of the data file sample dat Reading model section from sample mod 32 lines were read Reading data section from sample dat 68 lines were read Generating nb Generating cost Model has been successfully generated SYMPHONY After loading the instance type solve to solve the corresponding integer program or lpsolve to solve its linear relaxation SYMPHONY solve kkkKK Found Better Feasible Solution kKKKK Cost 40 000000 AR K K K K K K K K FK FK FK FK FK FK K K K K 2K dd FK FK FK FK FK K K K dale FK FK FK K K K K K ok K Optimal Solution Found FECA kk k k ak ak ak ak ak ak k CI A A A ak k k k KK A A A A k k KK Kk k k 2K EO KK K aK 23 SYMPHONY lpsolve kkkKK Found Better Feasible Solution door Cost 43 000000 AO FK K K K K K K K K K FK K FK FK K K K K K 2K FK FK 2g FK FK FK FK K K K K FK FK FK FK K K K a K 2K K ok K Optimal Solution Found kak
27. gt user_finished_checking_cuts int user_finished_checking_cuts void user Description When this function is invoked there are no more cuts to be checked so the user can dis mantle data structures he created in user_prepare_to_check_cuts Also if he received and stored the LP solution himself he can delete it now Arguments void user IN Pointer to the user defined data structure Return values USER_ERROR Ignored USER_SUCCESS The user function exited properly USER_DEFAULT There are no user defined cuts in the pool Invoked from After all cuts have been checked 214 gt user_free_cp int user_free_cp void user Description The user has to free all the data structures within user and also free user itself The user can use the built in macro FREE that checks the existence of a pointer before freeing 16 Arguments void user INOUT Pointer to the user defined data structure should be NULL on exit Return values USER_ERROR Ignored USER_SUCCESS The user freed all data structures USER_DEFAULT There is nothing to be freed Invoked from cp close at module shutdown 215 6 3 5 Draw graph module callbacks Due to the relative simplicity of the cut pool there are no wrapper functions implemented for DG Consequently there are no default options and no post processing gt user _dg process _message void user_dg_process_message void user window win FILE write_to Description The user has to proc
28. if the user wants to provide an estimate of the optimal value which will help guide the search This is used in conjunction with the diving strategy BEST_ESTIMATE tm_exe dg_exe strings tm dg The name of the executable files of the TM and DG modules Note that the TM executable name may have extensions that depend on the con figuration of the modules but the default is always set to the file name produced by the makefile If you change the name of the treemanager executable from the default you must set this parameter to the new name tm debug dg debug boolean both FALSE Whether these modules should be started under a debugger or not see 5 6 2 for more details on this tm machine string empty string On which processor of the virtual machine the TM should be run Leaving this parameter as an empty string means arbitrary selection do_draw graph boolean FALSE Whether to start up the DG module or not see Section 5 6 4 for an introduction to this do_branch_and_cut boolean TRUE Whether to run the branch and cut algorithm or not Set this to FALSE to run the user s heuristics only mc_search order integer MC_FIFO Use the fifo MC_FIFO or lifo MC_LIFO searh order during the multi criteria solution procedure mc_warm start boolean FALSE Whether to solve the corresponding problem of each iteration from a warm start loaded from a base iteration which is the first iteration w
29. inherent in BCP In the remainder of this section we further discuss this distinction and its implications 4 3 2 1 Variables In SYMPHONY problem variables are represented by a unique global index assigned to each variable by the user This index represents each variable s position in a virtual global list known only to the user The main requirement of this indexing scheme is that given an index and a list of active cuts the user must be able to generate the corresponding column to be added to the matrix As an example in problems where the variables correspond to the edges of an underlying graph the index could be derived from a lexicographic ordering of the edges when viewed as ordered pairs of nodes This indexing scheme provides a very compact representation as well as a simple and effective means of moving variables in and out of the active set However it means that the user must have a priori knowledge of all problem variables and a method for indexing them For combinatorial models 39 such as the Traveling Salesman Problem this does not present a problem However for some set partitioning models for instance the number of columns may not be known in advance Even if the number of columns is known in advance a viable indexing scheme may not be evident Eliminating the indexing requirement by allowing variables to have abstract user defined representations such as we do for cuts would allow for more generality but
30. invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 149 gt sym_get_ub_for_new_obj int sym_get_ub_for_new_rhs sym_environment env int cnt int new_obj_ind double new_obj_val double ub_for_new_obj Description This routine is used for a basic sensitivity analysis of the objective function case It returns a quick lower bound for the problem with a modified objective vector using the information gathered from the branching tree of the original solved problem Note that in order to use this feature the sensitivity_analysis parameter needs to be set before solving the original problem Arguments sym_environment env IN Pointer to the SYMPHONY environment int cnt IN The number of the non zero elements in the new ob jective coefficients int new_obj_ind IN Array of the column indices of these non zero ele ments double new_obj_val IN Array of the values of these non zero elements double ub_for_new_obj OUT Pointer to a double indicating the upper bound ob tained for the new problem This value will be set to SYM_INFINITY if an upper bound can not be found Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 150 6 2 Callable Library C API SYMPHONY s C interface is derived from COIN OR s Open Solver Interface OSI The OSI methods are implemented simply as wrapped calls to the SYM
31. is due to either an invalid cut of an invalid branching Note that this feature only works when branching on binary variables See Section 5 6 3 for more on how to use this feature Arguments void user IN Pointer to the user defined data structure int feas_sol_size INOUT Pointer to size of the feasible solution passed by the user int feas_sol INOUT Pointer to the array of user indices containing the feasible solution This array is simply copied by the tree manager and must be freed by the user Return values USER_ERROR Solution tracing is not enabled Arguments USER_SUCCESS Tracing of the given solution is enabled USER_DEFAULT No feasible solution given 167 gt user_process_Own_messages int user_process_own_messages void user int msgtag Description The user must receive any message he sends to the master module independently of SYMPHONY s own messages An example for such a message is sending feasible solutions from separate heuristics processes fired up in user_start_heurs Arguments void user IN Pointer to the user defined data structure int msgtag IN The message tag of the message Return values USER_ERROR Ignored USER SUCCESS Message is processed USER_DEFAULT No user message types defined 168 gt user_free_master int user_free_master void user Description The user frees all the data structures within user and also free user itself This can be done using the built in macro FRE
32. k 2k ak ak ak ak ak ak K 2K ak 3k ak 2k ak ak K 2K 3k 3K 3K ak ak 2k 2K 2K 2k 2K 3K 2K 2K K K 2K 2K 2K 2K 2K 2k K gt K 2K K K ak K 2K SYMPHONY As above only the objective values of the feasible solutions found so far and the termination code of the solution process will be displayed see Section 3 1 4 for displaying more output 3 1 4 Set Menu The Set submenu is used to set SYMPHONY s run time parameters To enter this submenu type set SYMPHONY set Please type help to see the list of parameters SYMPHONYASet You can override the default value of a parameter by typing the name of the parameter You will then be prompted to enter the new value of that parameter For instance in order to display more outputs during the solution process you need to set the verbosity parameter set to 1 by default for the interactive shell routines to a nonnegative integer SYMPHONY Set verbosity Value of the parameter 3 Setting verbosity to 3 SYMPHONY Set A confirmation message will also be displayed Note that typing help or displays only a subset of the most commonly used run time parameters However you are allowed to set any of the pa rameters given in Section 6 4 Additionally you can set the values of parameters using a parameter file as an input In such a file the new value of each parameter must follow the name of that parameter For instance suppose that the my_param file consists of the following lines
33. node set up costs When considering these additional factors it is sometimes be more effective to deviate from the best first search order We discuss the importance of such strategies below 4 4 3 2 Search Chains and Diving One reason for not strictly enforcing the search order is because it is somewhat expensive to construct a search node send it to an NP module and set it up for processing If after branching we choose to continue processing one of the children of the current subproblem we avoid the set up cost as well as the cost of communicating the node description of the retained child subproblem back to the tree manager This is called diving and the resulting chain of nodes is called a search chain There are a number of rules for deciding when an NP module should be allowed to dive One such rule is to look at the number of variables in the current LP solution that have fractional values When this number is low there may be a good chance of finding a feasible integer solution quickly by diving This rule has the advantage of not requiring any global information We also dive if one of the children is close to being the best node where close is defined by a chosen parameter In addition to the time saved by avoiding reconstruction of the LP in the child diving has the advantage of often leading quickly to the discovery of feasible solutions as discussed above Good upper bounds not only allow earlier pruning of unpromis
34. objective value Choose child with the lowest objective value Choose child with the most fractional variables Frac tional branching options are only available if the frac tional branching compile time option is set in the make file Choose child with the lowest number of fractional vari ables Checks which children can be fathomed based on the objective value of their pre solved LP relaxation Wrapper invoked from branch 189 gt user_print_branch_stat int user_print_branch_stat void user branch_obj can cut_data cut int n var_desc vars char action Description Print out information about branching candidate can such as a more explicit problem specific description than SYMPHONY can provide for instance end points of an edge If verbosity is set high enough the identity of the branching object and the children with objective values and termination codes for the pre solved LPs is printed out to the standard output by SYMPHONY Arguments void user IN Pointer to the user defined LP data structure branch obj can IN The branching candidate cut_data cut IN The description of the cut if the branching object is a cut int n IN Number of variables var_desc vars IN Array of variables in the current relaxation char action IN Array of actions for the children Return values USER_ERROR Error Ignored by SYMPHONY USER_SUCCESS The user printed out whatever she wanted to USER DEFAULT
35. primarily toward support of solvers for linear programming LP problems This is because LP solvers employing some version of the simplex algorithm support much richer functionality and a wider range of interface functions due to their support of warm starting from previously saved checkpoints This functionality is difficult to provide for MILP solvers In SYMPHONY 5 2 3 we have implemented for MILPs some of the same functionality that has long been available for LP solvers As such our OSI interface supports warm starting and sensitivity analysis The implementations of this functionality is straightforward at the moment but will be improved in future versions 3 3 3 Linking to the Callable Library To link your program to the callable library make sure you have included the header file symphony h in all the source files that call SYMPHONY functions Also make sure that your include path contains the directory where all of SYMPHONY s header files are stored Then simply include the appropriate SYMPHONY library in the set of libraries to be linked and make sure that the path to the library is in the library path Example makefiles For Unix like environments are included in the Examples directory 32 3 4 Using the Callback Functions The user s main avenues for customization of SYMPHONY are the tuning of parameters and the implementation of one or more of over 50 user callback functions The callback functions allow the user to override
36. probing cuts using COIN s cut generation library generate_cgl_clique_cuts boolean TRUE Whether or not to generate clique cuts using COIN s cut generation library generate cgl flow and cover_cuts boolean FALSE Whether or not to generate flow and cover cuts using COIN s cut generation library generate_cgl_rounding cuts boolean FALSE Whether or not to generate simple rounding cuts using COIN s cut generation library generate_cgl_lift_and_project_cuts boolean FALSE Whether or not to generate lift and project cuts using COIN s cut generation library 232 fp_enabled integer SYM_FEAS_PUMP_DEFAULT 1 Determines the overall policy of using the feasibility pump heuristic to find feasible solutions SYM_FEAS_PUMP_DEFAULT 1 indi cates that the decision to use the heuristic is determined on the basis of current val ues Of lower bound upper bound the time used etc based on some preset rules SYM_FEAS PUMP _REPEATED 2 indicates that the heuristic will be used every few iterations until the problem is solved The frequency can be adjusted through the fp_frequency pa rameter SYM_ FEAS PUMP_TILL_SOL 3 indicates that the heuristic is used only until the first feasible solution is found SYM_FEAS_PUMP_DISABLE 1 indicates that the heuristic is not used fp_frequency integer 10 Determines the number of LPs that are solved before which the feasibility pump heuristic is called again This paramet
37. process must run on a certain type of machine due to resource requirements use_cg boolean FALSE Whether to use a cut generator or not TM_random_seed integer 17 The random seed used in the TM unconditional_dive_frac double 0 0 The fraction of the nodes on which SYMPHONY randomly dives unconditionally into one of the children diving strategy integer BEST_ESTIMATE O The strategy employed when deciding whether to dive or not The BEST_ESTIMATE 0 strategy continues to dive until the lower bound in the child to be dived into exceeds the parameter upper_bound_estimate which is given by the user The COMP_BEST_K 1 strategy computes the average lower bound on the best diving_k search tree nodes and decides to dive if the lower bound of the child to be dived into does not exceed this average by more than the fraction diving threshold The COMP BEST K GAP 2 strategy takes the size of the gap into account when decid ing whether to dive After the average lower bound of the best diving_k nodes is computed the gap between this average lower bound and the current upper bound is computed Diving only occurs if the difference between the computed average lower bound and the lower bound of the child to be dived into is at most the fraction diving threshold of the gap Note that fractional diving settings can override these strategies See below diving_k diving threshold integer double 1 0 05 See above fract
38. start class for MILPs The main content of the class is a compact description of the search tree at the time the computation was halted This description contains complete information about the subproblem corresponding to each node in the search tree including the branching decisions that lead to the creation of the node the list of active variables and constraints and warm start information for the subproblem itself which is a linear program All information is stored compactly using SYMPHONY s native data structures which store only the differences between a child and its parent rather than an explicit description of every node This approach reduces the tree s description to a fraction of the size it would otherwise be In addition to the tree itself other relevant information regarding the status of the computation is recorded such as the current bounds and best feasible solution found so far Using the warm start class the user can save a warm start to disk read one from disk or restart the computation at any point after modifying parameters or the problem data itself This allows the user to easily implement periodic checkpointing to design dynamic algorithms in which the parameters are modified after the gap reaches a certain threshold or to modify problem data during the solution process if needed Modifying Parameters The most straightforward use of the warm start class is to restart the solver after modifying problem parameters
39. startup project by choosing Set as Startup Project from the Project menu after selecting the symphony project in the Solution Ex plorer Choose Build Solution from the Build menu This should successfully build the SYMPHONY library and the corresponding executable e To test the executable go to the Debug tab and choose Start Without Debugging This should open a Window in which the interactive solver will appear Type help or to see a list of available commands or see Chapter 3 for instructions on using the interactive solver Note that there is some functionality missing from the Windows version Most prominently the timing functions do not work In addition the Windows version will only run in sequential mode for a variety of reasons However it should be relatively easy to get it running in parallel if you can get PVM working under Windows 2 2 3 2 Building in a Windows Terminal These instructions are for MSVC Version 8 Instructions for other versions should be similar e Open a command terminal choose Run on the Start menu and type cmd in the dialogue box Go to the MSVisualStudio v8 directory and type devenv symphony sln build all This will create both the debug and release versions of SYMPHONY If you want to compile only one of them type devenv symphony sln build all debug or devenv symphony sln build all release For each command the library 1ibSymphony lib and the executable symphony will be cre ated
40. sym_solve sym_warm_solve solve the MILP problem from scratch or from a warm start if loaded resolve sym_warm_solve re solve the MILP problem after some modifications initialSolve sym_solve solve the MILP problem from scratch multiCriteriaBranchAndBound sym_mc_solve solve he multi criteria problem setInitialData sym_set_defaults set he parameters to their defaults parseCommandLine sym_parse_command_line read the command line arguments findInitialBounds sym_find_initial_bounds find the initial bounc s via the user defined heuristics createPermanentCutPools sym_create_permanent_cut_pools save the global cuts loadProblem sym_explicit_load_problem oad the problem through a set of arrays getWarmStart sym_get_warm start get the warm start description setWarmStart sym_set_warm_start set the warm start description getLbForNewRhs sym_get_1b_for_new rhs find a lower bound to the new rhs problem using the post solution info getUbForNewRhs sym_get_1b_for_new_rhs find an upper bound to the new rhs problem using the post solution info getLbForNewObj sym_get_lb_for_ new_rhs find a lower bound to the new obj problem using the post solution info get UbForNewObj sym_get_1b_for_new rhs find an upper bound to the n
41. the column upper bounds Note that the size of this array has to be at least the number of columns Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 105 gt sym_get_row_sense int sym_get_row_sense sym_environment env char rowsen Description This routine is used to get the row senses Arguments sym_environment env IN Pointer to the SYMPHONY environment char rowsen OUT Pointer to a char type array to be filled by the row senses Note that the size of this array has to be at least the number of rows Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 106 gt sym_get_rhs int sym_get_rhs sym_environment env double rowrhs Description This routine is used to get the right hand side vector Arguments sym_environment env IN Pointer to the SYMPHONY environment double rowrhs OUT Pointer to a double type array to be filled by the right hand side vector Note that the size of this array has to be at least the number of rows Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 107 gt sym_get_row_range int sym_get_row_range sym_environment env double rowrng Description This routine is used to get the row ranges Arguments sy
42. the vars and x arrays var_desc vars IN The variables currently in the LP relaxation double x IN Values of the above variables int where IN Where the solution is to be sent LP_SOL_TO CG or LP_SOL_TO_CP Return values USER_ERROR Error No message will be sent USER_SUCCESS User packed and sent the message USER_DEFAULT Regulated by the pack_lp_solution_default parameter initially set to SEND_NOZEROS SEND_NONZEROS Send user indices and values of variables at nonzero level SEND_FRACTIONS Send user indices and values of variables at fractional level Wrapper invoked from fathom_branch after an LP relaxation has been solved The message is always sent to the cut generator if there is one The message is sent to the cut pool if a search tree node at the top of a chain is being processed except at the root in the first phase or if a given number cut_pool_check_freq of LP relaxations have been solved since the last check Note The wrapper automatically packs the level index and iteration number corresponding to the current LP solution within the current search tree node as well as the objective value and upper bound in case the solution is sent to a cut generator This data will be unpacked by SYMPHONY on the receiving end the user will have to unpack there exactly what he has packed here 195 gt user_logical_fixing int user_logical_fixing void user int varnum var_desc vars double x char status int
43. the basis unnecessarily it is usually advisable to adopt an aggressive strategy for row removal 4 4 2 3 Branching Branching takes place whenever either 1 both cut generation and column generation if performed have failed 2 tailing off in the objective function value has been detected or 3 the user chooses to force branching Branching can take place on cuts or variables and can be fully automated or fully controlled by the user as desired Branching can result in as many children as the user desires though two is typical Once it is decided that branching will occur the user must either select the list of candidates for strong branching see below for the procedure or allow SYMPHONY to do so automatically by using one of several built in strategies such as branching on the variable whose value is farthest from being integral The number of candidates may depend on the level of the current node in the tree it is usually best to expend more effort on branching near the top of the tree After the list of candidates is selected each candidate is pre solved by performing a specified number of iterations of the dual simplex algorithm in each of the resulting subproblems Based on the objective function values obtained in each of the potential children the final branching object is selected again either by the user or by built in rule This procedure of using exploratory LP information in this manner to select a branching candidat
44. the file and a list of all parameters is given in Section 6 4 27 The output level can be controlled through the use of the verbosity parameter which can be invoked Setting this parameter at different levels will cause different progress messages to be printed out Level 0 only prints out the introductory and solution summary messages along with status messages every 10 minutes Level 1 prints out a message every time a new node is created Level 3 prints out messages describing each iteration of the solution process Levels beyond 3 print out even more detailed information To get no output at all the verbosity level must be set to 2 3 3 Using the Callable Library SYMPHONY s callable library consists of a complete set of subroutines for loading and modifying problem data setting parameters and invoking solution algorithms The user invokes these sub routines through the API specified in the header file symphony_api h Some of the basic commands are described below For the sake of brevity the arguments have been left out 3 3 1 The C API sym_open_environment Opens a new environment and returns a pointer to it This pointer then has to be passed as an argument to all other API subroutines in the C interface this pointer is maintained for the user sym_parse command line Invokes the built in parser for setting commonly used parameters such as the file name which to read the problem data via command line switches A call
45. the tradeoffs between competing objec tives the method can be used to perform detailed sensitivity analysis in a manner analogous to that which can be done with simplex based solvers for linear programs As an example suppose we would like to know exactly how the optimal objective function value for a given pure integer program depends on the value of a given objective function coefficient Consider increasing the objective function coefficient of variable from its current value Taking the first objective function to be the original one and taking the second objective function to be the unit vector we can derive the desired sensitivity function by using the bicriteria solution algorithm to enumerate all supported solutions and breakpoints This information can easily be used to obtain the desired function Figure 3 4 shows the code for performing this analysis on variable 0 In addition to the parts of the API we have just described there are a number of standard subrou tines for accessing and modifying problem data and parameters These can be used between calls to the solver to change the behavior of the algorithm or to modify the instance being solved These modifications are discussed in more detail in Section 4 4 1 2 30 int main int argc char argv warm_start_desc ws sym_environment env sym_open_environment sym_parse_command_line env argc argv sym_load_problem env sym_set_int_param env node_limit 1
46. to extravarnum are put in the extra set The user must set extravarnum unless an MPS or GM PL AMPL file was read in by SYMPHONY Post processing The array of base and extra indices are sorted 161 gt user_receive_feasible_solution int user_receive_feasible_solution void user int msgtag double cost int numvars int indices double values Description This function is only used for parallel execution Feasible solutions can be sent and or stored in a user defined packed form if desired For instance the TSP a tour can be specified simply as a permutation rather than as a list of variable indices In the LP module a feasible solution is packed either by the user or by a default packing routine If the default packing routine was used the msgtag will be FEASIBLE_SOLUTION_NONZEROS In this case cost numvars indices and values will contain the solution value the number of nonzeros in the feasible solution and their user indices and values The user has only to interpret and store the solution Otherwise when msgtag is FEASIBLE SOLUTION USER SYMPHONY will send and receive the solution value only and the user has to unpack exactly what she has packed in the LP module In this case the contents of the last three arguments are undefined In most cases SYMPHONY s default routines for sending and receiving feasible solutions as well as displaying them will suffice These routines simply display all nonzeros by either index o
47. to this subroutine instructs SYMPHONY to parse the command line and set the appropriate parameters This subroutine also sets all other parameter values to their defaults so it should only called when this is desired sym_load_problem Reads the problem data and sets up the root subproblem This includes specifying which cuts and variables are in the core those that are initially present in every sub problem during the search process and the additional cuts and variables to be initially active in the root subproblem By default SYMPHONY reads an MPS or GMPL file specified by the user but the user can override this default by implementing a user callback that reads the data from a file in a customized format see Section 3 4 sym_find initial bounds Invokes the user callback to find initial bounds using a custom heuristic sym_solve Solves the currently loaded problem from scratch This method is described in more detail in Section 4 4 1 1 28 int main int argc char argv sym_environment env sym_open_environment sym_parse_command_line env argc argv sym_load_problem env sym_solve env sym_close_environment env J Figure 3 1 Implementation of a generic MILP solver with the SYMPHONY C callable library sym warm solve Solves the currently loaded problem from a warm start This method is de scribed in more detail in Section 4 4 1 2 sym_mc_solve Solves the currently loaded problem as a mu
48. 00 sym_set_int_param env keep_warm_start TRUE sym_solve env ws sym_get_warm_start env sym_set_int_param env node_limit 1 sym_warm_solve env sym_set_obj_coeff env 0 100 sym_set_obj_coeff env 200 150 sym_set_warm_start ws sym_warm_solve env Figure 3 3 Use of SYMPHONY s warm start capability 3 3 2 The C API The Open Solver Interface OSI is a C class that provides a standard API for accessing a variety of solvers for mathematical programs It is provided as part of the COIN OR repository 24 along with a collection of solver specific derived classes that translate OSI call into calls to the underlying libraries of the solvers A code implemented using calls to the methods in the OSI base class can easily be linked with any solver for which there is an OSI interface This allows development of solver independent codes and eliminates many portability issues The current incarnation of OSI supports only solvers for linear and mixed integer linear programs although a new version supporting a wider variety of solvers is currently under development We have implemented an OSI interface for SYMPHONY 5 2 3 that allows any solver built with int main int argc char argv sym_environment env sym_open_environment sym_parse_command_line env argc argv sym_load_problem env sym_set_obj2_coeff env 0 1 sym_mc_solve env hi Figure 3 4 Performing sensitivity analy
49. ATED_ABNORMALLY Function invoked unsuccessfully 125 gt sym_set_col_lower double sym_set_col_lower sym_environment env int index double value Description This routine is used to set the lower bound of a variable Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN Index of the variable Note that it has to be at most the number of columns double value IN New lower bound of the variable Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 126 gt Sym _set _col_upp er double sym_set_col_upper sym_environment env int index double value Description This routine is used to set the upper bound of a variable Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN Index of the variable Note that it has to be at most the number of columns double value IN New upper bound of the variable Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 127 gt sym_set_row_lower double sym_set_row_lower sym_environment env int index double value Description This routine is used to set the lower bound of a row Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN Index of the row Note that it has to be at most the number of rows
50. C Ferris and J T Linderoth Fatcop 2 0 Advanced features in an opportunistic mixed integer programming solver Annals of Operations Research 103 17 32 2001 J Climaco C Ferreira and M E Captivo Multicriteria integer programming an overview of different algorithmic approaches In J Climaco editor Multicriteria Analysis pages 248 258 Springer Berlin 1997 C Cordier H Marchand R Laundy and L A Wolsey bc opt A branch and cut code for mixed integer programs Mathematical Programming 86 335 353 1999 ILOG CPLEX Division http www cplex com J Eckstein C A Phillips and W E Hart PICO An object oriented framework for parallel branch and bound Technical Report RRR 40 2000 Rutgers University 2000 M Ehrgott and X Gandibleux A survey and annotated bibliography of multiobjective com binatorial optimization OR Spektrum 22 425 460 2000 M Ehrgott and X Gandibleux Multiobjective combinatorial optimization theory method ology and applications In M Ehrgott and X Gandibleux editors Multiple Criteria Optimization State of the Art Annotated Bibliographic Surveys pages 369 444 Kluwer Aca demic Publishers Boston MA 2002 237 13 M Ehrgott and M M Wiecek Multiobjective programming In M Ehrgott J Figueira and 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 S Greco editors State of the Art of Multiple Criteria Decision Analy
51. CTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED NORMALLY Function invoked successfully 84 gt sym_get_user_data int sym_get_user_data sym_environment env void user Description This routine is used to get the user s problem data structure from SYMPHONY environment Arguments sym_environment env INOUT Pointer to the SYMPHONY environment void user OUT Pointer to the user defined problem structure Return values ERROR__USER Error in the passed in user structure FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 85 gt sym_close_environment int sym_close_environment sym_environment env Description This routine closes the environment and returns the allocated memory Arguments sym_environment env INOUT Pointer to the SYMPHONY environment Return values ERROR__USER Error User error detected in user_free_master func tion FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_ NORMALLY Function invoked successfully 86 6 1 2 Parameter Query and Modification gt sym_set_defaults int sym_set_defaults sym_environment env Description This routine sets all the environment variables and parameters to their default values Arguments sym_environment env INOUT Pointer to the SYMPHONY environment to be mod ified Return values FUNCTION TERMINATED ABNORMALLY F
52. CTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 141 gt sym_read_warm_start int sym_read_warm_start char file warm_start_desc ws Description This routine is used to read in a warm start structure from a file Arguments char file IN The name of the file the warm start is desired to be read from warm_start_desc ws OUT Pointer to a warm start object to be read from the file Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 142 gt sym_delete_warm_start void sym_delete_warm_start warm_start_desc ws Description This routine is used to free a warm start structure and return the allocated memory Arguments warm_start_desc ws IN Pointer to the warm start description to be deleted 143 gt sym_get_warm_start warm_start_desc sym_get_warm_start sym_environment env int copy_warm_start warm_start_desc ws Description This routine is used to get the warm start description loaded to the environment Arguments sym_environment env IN Pointer to the SYMPHONY environment int copy warm_start IN A boolean indicating whether the warm start of the environment is desired to be copied or overtaken warm_start_desc ws OUT Pointer to a pointer to be directed to a copy or the itself of the currently loaded warm start Return values FUNCTION_T
53. DISP_FEAS_SOLUTION indicates a solution feasible to the original IP problem DISP_RELAXED_SOLUTION indicates the solution to any LP relaxation and DISP_FINAL_RELAXED SOLUTION indicates the solution to an LP relaxation when no cut has been found There is no post processing Default options print out user indices and values of nonzero or fractional variables on the standard output Arguments void user int which_sol int varnum int indices double values Return values USER_ERROR USER_SUCCESS USER_DEF AULT DISP_NOTHING DISP_NZ_INT DISP_NZ_HEXA DISP_FRAC_INT DISP_FRAC_HEXA IN Pointer to the user defined LP data structure IN The type of solution passed on to the displaying function Possible values are DISP_FEAS_SOLUTION DISP_RELAXED_SOLUTION and DISP_FINAL_RELAXED_SOLUTION IN The number of variables in the current solution at nonzero level the length of the indices and values arrays IN User indices of variables at nonzero level in the current solu tion IN Values of the nonzero variables Error SYMPHONY ignores error message User displayed whatever she wanted to Regulated by the parameter display_solution_default but set to DISP_NZ_INT unless overridden by the user Display nothing Display user indices as integers and values of nonzero variables Display user indices as hexadecimals and values of nonzero vari ables Display user indices as integers and values of variables not at their lowe
54. E that checks the existence of a pointer before freeing it Arguments void user INOUT Pointer to the user defined data structure should be NULL on return Return values USER_ERROR Ignored This is probably not a fatal error USER_SUCCESS Everything was freed successfully USER_DEFAULT There is no user memory to free 169 6 3 2 LP module callbacks 6 3 2 1 Data Structures We first describe a few structures that are used to pass data into and out of the user functions of the LP module gt MIPdesc One of the few internally defined data structures that the user has to deal with frequently is the MIPdesc data structure which holds the data needed to describe a MILP This data structure is used by SYMPHONY for two purposes First it is used to store the description of a generic MILP that is either read in from an MPS or AMPL file More importantly it is the data structure the user must use to pass the subproblem descriptions back to SYMPHONY at the time a new search tree node is created in the function user_create_subproblem see Section 6 3 2 2 The structure has 14 fields listed below that must be filled out to describe a subproblem some fields are optional A subproblem is a mixed integer program defined by a matrix of constraints an ob jective function bounds on both the right hand side values of the constraints and the variables an array indicating which variables are integer and optionally an array of column nam
55. ERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 144 gt sym_set_warm_start int sym_set_warm_start sym_environment env warm_start_desc ws Description This routine is used to load a warm start structure to the environment Arguments sym_environment env INOUT Pointer to the SYMPHONY environment warm_start_desc ws IN Pointer to the warm start structure to be loaded to the environment Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 145 gt sym_create_copy_warm_start warm_start_desc sym_create_copy_warm_start warm_start_desc ws Description This routine is used to copy the given warm start structure Arguments warm_start_desc ws INOUT Pointer to the warm start structure to be copied Return values tt NULL An empty warm start description is passed in WARM_START_DESC Pointer to the copy of the warm start structure 146 6 1 7 Sensitivity Analysis Functions gt sym_get_lb_for_new_rhs int sym_get_lb_for_new_rhs sym_environment env int cnt int new_rhs_ind double new_rhs_val double lb_for_new_rhs Description This routine is used for a basic sensitivity analysis of the right hand side case It returns a lower bound for the problem with a modified right hand side using the information gathered from the branching tree of the original solv
56. F SYMPHONY Datasets sample mod D SYMPHONY Datasets sample dat For more MPS data files for further testing see the MIPLIB library in the Data subdirectory To run the interactive optimizer execute SYMPHONY without any command line arguments 1 e type bin symphony and then type help or to see a list of available commands After the SYMPHONY library and the executable are compiled and tested you can type make clean if you want to save disk space That s it Now you are ready to use SYMPHONY callable library or solve generic MILP problems through the executable 12 2 2 2 4 Building for parallel architectures Shared memory architectures To compile a shared memory version of SYMPHONY simply use an OpenMP compliant compiler Version 5 2 3 has been tested with gcc 4 2 and should build by configuring with configure enable openmp After configuring follow the earlier instructions for building and testing To invoke SYMPHONY from the command line with multiple threads specify the number of threads with the p option Les bin symphony p 2 F SYMPHONY Datasets sample mps Distributed memory architectures To compile a distributed application it is necessary that PVM be installed either in the system path or in the directory pointed to by the environment variable PVM_ROOT this can be your home directory if PVM is not already installed on your network To install PVM yourself the current version can be obtained at http www
57. IND 198 gt user_generate_cuts_in_lp int user_generate_cuts_in_lp void user LPdata lp_data int varnun Description var_desc vars double x int new_row_nunm cut_data cuts The user might decide to generate cuts directly within the LP module instead of using the cut generator This can be accomplished either through a call to this function or simply by configuring SYMPHONY such that the cut generator is called directly from the LP solver One example of when this might be done is when generating Gomory cuts or something else that requires knowledge of the current LP tableau The user must return the list of generated cuts by allocating an array of cut_data structures and setting cuts to point to this array Post processing consists of checking if any of the new cuts are already in the local pool or dominated by a cut in the local pool Arguments void user LPdata lp_data int varnum var_desc vars double x int new_row_num cut_data xcuts Return values USER_ERROR USER_SUCCESS USER DEFAULT GENERATE_CGL_CUTS DO_NOT_GENERATE_CGL_CUTS Post processing IN IN IN IN IN OUT OUT Pointer to the user defined LP data structure A pointer to SYMPHONY s internal data struc ture for storing the LP relaxation and related data The number of variables currently in the LP relaxation The length of the vars and x ar rays The variables currently in the LP relaxation Values of t
58. J unless overridden by the user Prefer the candidate with the biggest difference between high est and lowest objective function values Prefer the candidate with the lowest minimum objective func tion value The minimum is taken over the objective function values of all the children Prefer the candidate with the highest minimum objective function value Prefer the candidate with the lowest maximum objective function value Prefer the candidate with the highest maximum objective function value Prefer the candidate with the lowest minimum number of fractional variables The minimum is taken over the number of fractional variables in all the children Fractional branching options are only available if the fractional branching compile time option is set in the makefile Prefer the candidate with the highest minimum number of fractional variables Prefer the candidate with the lowest maximum number of fractional variables Prefer the candidate with the highest maximum number of fractional variables Wrapper invoked from select_branching object after the LP relaxations of the chil 188 gt user_select_child int user_select_child void user double ub branch_obj can char action Description By the time this function is invoked the candidate for branching has been chosen Based on this information and the current best upper bound the user has to decide what to do with each child Possible actions for a child are KEEP
59. MILP solver This process will create 1 a generic callable library that allows SYMPHONY to be called from a C or C code and 2 an executable that can be used as a stand alone application to solve MILPs written in either MPS or GMPL file format SYMPHONY can be further customized by implementing one of more than 50 callback functions that change SYMPHONY s default behavior For information on customizing SYMPHONY using callbacks a quick start guide is provided below More detailed information is provided in Chapter 5 9 2 2 1 External Dependencies 2 2 1 1 The LP Engine SYMPHONY requires the use of a third party callable library to solve the LP relaxations once they are formulated The LP solver is called through Open Solver Interface also available from COIN https projects coin or org 0si The list of solvers with OSI interfaces currently numbers eight and includes both commercial and open source alternatives By default SYMPHONY now uses the COIN LP solver Clp However if another LP solver is desired this is possible by changing the configuration settings 2 2 1 2 GMPL Parser If you would like to be able to parse GMPL models GMPL is a subset of AMPL you will need to install GLPK http www gnu org software glpk To do so automatically run the get Glpk script in the ThirdParty G1lpk directory After that Glpk should be built and linked automatically enabling the ability to read GMPL files 2 2 1 3 COIN Libraries S
60. MPHONY Interactively 3 1 1 Unix like Environments If you are planning to use the interactive optimizer in a Unix like environment and you are building SYMPHONY from source it is recommended that you run the configuration script see Section 2 2 2 2 with the command line argument that enables GNU packages i e configure enable gnu packages This will allow the interactive shell to behave exactly like a Linux terminal command line i e it will keep the history of the used commands will do command completion etc Note that you must have the required packages readline and history installed To use SYMPHONY s interactive shell run the executable without any command line arguments i e type bin symphony You will enter a command shell environment where you will be prompted for inputs The user interface consists of a main menu where an instance is read in and solved a set menu where parameters are set and a display menu where results statistics and parameter values are displayed 3 1 2 Microsoft Windows To invoke SYMPHONY s interactive solver in an Microsoft Windows environment simply double click on the symphony exe file in Windows Explorer This should open a terminal window in which the solver will run Note that if you built SYMPHONY in CYGWIN without the option enable dos compile then you will have to have the CYGWIN DLL in your path in order for the executable to run 21 3 1 3 Main Menu Below is the
61. N The number of cuts added waiting row rows IN Array of waiting rows containing the cuts added Return values USER_ERROR Revert to default USER_SUCCESS User printed whatever he wanted USER_DEFAULT Print out the number of cuts added Wrapper invoked from add best waiting rows after it has been decided how many cuts to add and after the cuts have been selected from the local pool 201 gt user_purge_waiting_rows int user_purge_waiting_rows void user int rownum waiting_row rows char delete_rows Description The local pool is purged from time to time to control its size In this function the user has the power to decide which cuts to purge from this pool if desired To mark the i waiting row an element of the pre pool for removal she has to set delete_rows i to be TRUE delete_rows is allocated before the function is called and its elements are set to FALSE by default Post processing consists of actually deleting those entries from the waiting row list and compressing the list The default is to discard the least violated waiting rows and keep no more than what can be added in the next iteration this is determined by the max_cut_num_per_iter parameter Arguments void user IN Pointer to the user defined LP data structure int rownum IN The number of waiting rows waiting row rows IN The array of waiting rows char delete_rows OUT An array of indicators showing which waiting rows are to be deleted R
62. NCTION_TERMINATED_NORMALLY Function invoked successfully 120 gt sym_get_obj_val double sym_get_obj_val sym_environment env double objval Description This routine is used to get the objective value after solving the problem Arguments sym_environment env IN Pointer to the SYMPHONY environment double objval OUT Pointer to a double indicating the post solution ob jective value Return values FUNCTION TERMINATED ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 121 gt sym_get_primal_bound double sym_get_primal_bound sym_environment env double ub Description This routine is used to get the a priori upper lower bound for the problem Arguments sym_environment env IN Pointer to the SYMPHONY environment double ub OUT Pointer to a double indicating the upper for min imization or lower for maximization bound ob tained through user defined primal heuristics Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 122 gt sym_get_iteration_count double sym_get_iteration _count sym_environment env int numnodes Description This routine is used to get the number of the analyzed nodes of the branching tree after solving the problem It can also be used to query the status of a loaded warm start Arguments sym_environment env IN Pointer to the S
63. OUT OUT OUT OUT OUT OUT needed USER_DEFAULT Pointer to the user defined LP data structure GENERATE NEXTIND or GENERATE REAL NEXTIND see description above The number of added rows in the LP formulation i e the total number of rows less the number of base con straints This is the length of the cuts array Description of the cuts corresponding to the added rows of the current LP formulation The user is supposed to know about the cuts corresponding to the base con straints The last variable processed 1 if there was none by SYMPHONY The next variable 1 if there are none known to SYMPHONY Pointer to the user index of the next variable 1 if there is none Values of the nonzero entries in the column of the next variable Sufficient space is already allocated for this array Row indices of the nonzero entries in the column Suf ficient space is already allocated for this array The length of the colval and colind arrays Objective coefficient corresponding to the next vari able Lower bound of the next variable Upper bound of the next variable Error The LP process is aborted User filled out real nextind and generated its column if No column generation is done Wrapper invoked from price_all_vars and restore lp feasibility Note colval colind collen and obj do not need to be filled out if real_nextind is the same as nextind and generate what is GENERATE_REAL_NEXT
64. PHONY C callable library just described For instance when an instance of the OSI interface class is constructed a call is made to sym_open_environment and a pointer to the environment is stored in the class and when the OSI object is destroyed sym_close_environment is called to destroy the environment object Most subsequent calls within the class can then be made without any arguments To fully support SYMPHONY s capabilities we have extended the OSI interface to include some other methods not in the base class For example we added calls equivalent to our sym_parse_command line and sym_find_initial_bounds Additionally SYMPHONY has a warm start class derived from the CoinWarmStart base class to support the new functionalities of the MILP warm starting such as sym_get_warm_start and sym_set_warm_start They are also implemented as wrapped calls to the C interface library In order to have the whole list of the methods and information regarding their usage see the OSI SYMPHONY interface and SYMPHONY warm start header files 0siSymSolverInterface hpp and SymWarmStart hpp Here we will give the table of the C library equivalent calls of the C interface routines with brief descriptions 151 C Interface C Interface Description OsiSymSolverInterface sym_open environment create a new environment loadProblem sym_load problem load the problem read trough an MPS or GMPL file branchAndBound
65. Pointer to a string of the name of the column Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 137 gt sym_add_row int sym_add_row sym_environment env int numelems int indices double elements char rowsen double rowrhs double rowrng Description This routine is used to add a new row to the original constraint matrix Arguments sym_ environment env INOUT Pointer to the SYMPHONY environment int numelems IN An integer indicating the non zero elements of the row int indices IN Pointer to an integer type array indicating the col umn indices of the non zero elements of the row and having a size of at least numelems double elements IN Pointer to a double type array indicating the values of the non zero elements of the row and having a size of at least numelems char rowsen IN A character indicating the sense of the row double rowrhs IN A double indicating the right hand side of the row double rowrng IN A double indicating the range value of the row Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 138 gt sym_delete_cols int sym_delete_cols sym_environment env int num int indices Description This routine is used to delete columns from the original problem description Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int num IN An
66. SS The user is prepared to check cuts USER_DEFAULT There are no user defined cuts in the pool Invoked from Whenever an LP solution is received 212 gt user_check_cut int user_check_cut void user double lpetol int varnun int indices double values cut_data cut int is_violated double quality Description The user has to determine whether a given cut is violated by the given LP solution see Section 6 3 2 for a description of the cut_data data data structure Also the user can assign a number to the cut called the quality This number is used in deciding which cuts to check and purge See the section on Cut Pool Parameters for more information Arguments void user INOUT The user defined part of p double lpetol IN The e tolerance in the LP module int varnum IN Same as the previous function int indices IN Same as the previous function double values IN Same as the previous function cut_data cut IN Pointer to the cut to be tested int is_violated OUT TRUE FALSE based on whether the cut is violated or not double quality OUT a number representing the relative strength of the cut Return values USER_ERROR Cut is not sent to the LP regardless of the value of is_violated USER_SUCCESS The user function exited properly USER DEFAULT Same as error Invoked from Whenever a cut needs to be checked Note The same note applies to number indices and values as in the previous function 213
67. SYMPHONY s default behavior for many of the functions performed as part of its algorithm The user has complete control over branching cutting plane generation management of the cut pool and the LP relaxation search and diving strategies etc More detailed information about using the callback functions to develop custom applications is provided in Chapter 5 33 34 Chapter 4 Technical Details 4 1 Branch and Bound Branch and bound is the broad class of algorithms from which branch cut and price is descended A branch and bound algorithm uses a divide and conquer strategy to partition the solution space into subproblems and then optimizes individually over each subproblem For instance let S be the set of solutions to a given problem and let c R be a vector of costs associated with members of S Suppose we wish to determine a least cost member of S and we are given S a good solution determined heuristically Using branch and bound we initially examine the entire solution space S In the processing or bounding phase we relax the problem In so doing we admit solutions that are not in the feasible set S Solving this relaxation yields a lower bound on the value of an optimal solution If the solution to this relaxation is a member of S or has cost equal to 3 then we are done either the new solution or respectively is optimal Otherwise we identify n subsets of S S1 Sn such that UF S S Each of these su
68. SYMPHONY 5 2 3 User s Manual T K Ralphs M Giizelsoy A Mahajan March 24 2010 This research was partially supported by NSF Grants DMS 9527124 DMI 0534862 and DMI 0522796 as well as Texas ATP Grant 97 3604 010 A revised version of Chapters 4 of this manual now appears in the Springer Verlag book Computational Combinatorial Optimization edited by M Jiinger and D Naddef see http link springer de link service series 0558 tocs t2241 htm 2Department of Industrial and Systems Engineering Lehigh University Bethlehem PA 18017 ted lehigh edu http www lehigh edu tkr2 3Department of Industrial and Systems Engineering Lehigh University Bethlehem PA 18017 megb lehigh edu http coral ie lehigh edu menal Mathematics and Computer Science Division Argonne National Lab Argonne IL 60439 mahajan mcs anl gov http www mcs anl gov mahajan 2000 2010 Ted Ralphs Acknowledgments First and foremost many thanks are due to Laci Lad nyi who worked with me on the development of a very early precursor of SYMPHONY called COMPSys many years ago now and who taught me much of what I then knew about programming Thanks are due also to Marta Eso who wrote an early draft of this manual for what was then COMPSys This release would not have been possible without the help of both Menal G zelsoy who has been instrumental in the development of SYMPHONY since version 4 0 and Ashutosh Mahajan who has worked on SYMPHONY since ve
69. The cut manager s primary job is to receive a solution from an NP module and return cuts from the pool that are violated by it The cuts are stored along with two pieces of information the level of the tree on which the cut was generated known simply as the level of the cut and the number of times it has been checked for violation since the last time it was actually found to be violated known as the number of touches The number of touches can be used as a simplistic measure of its effectiveness Since the pool can get quite large the user can choose to scan only cuts whose number of touches is below a specified threshold and or cuts that were generated on a level at or above the current one in the tree The idea behind this second criterion is to try to avoid checking cuts that were not generated nearby in the tree as they are less likely to be effective Any cut generated at a level in the tree below the level of the current node must have been generated in a different part of the tree Although this is admittedly a naive method it does seem to work reasonably well 53 On the other hand the user may define a specific measure of quality for each cut to be used instead For example the degree of violation is an obvious candidate This measure of quality must be computed by the user since the cut pool module has no knowledge of the cut data structures The quality is recomputed every time the user checks the cut for violation and a runnin
70. This sample code is an implementation of a basic MILP solver using SYMPHONY s C callable functions with user defined input see Section 3 3 To test the code go to the examples directory and type make milp milp milpOsi c This sample code is an implementation of a basic MILP solver using SYM PHONY s C callable functions through OsiSym interface with user defined input see Section 3 3 2 To test the code go to examples directory and type make milpOsi milpOsi If everything is working properly the libraries executables and header files can be installed in appropriate system directories if desired This must be done by hand This step will be done automatically if building from source using the automatic scripts provided see below 2 1 2 Installation for Use With Microsoft Visual C These instructions are for use with Microsoft Visual Studio 8 The procedure for other version of Vi sual Studio should be similar Download and unpack the archive symphony 5 2 3 win32 msvc6 zip e First test the executable by opening Windows Explorer and double click on symphony exe in the bin directory in the folder in which the distribution was unpacked This should open a Window in which the interactive solver will appear Type help or to see a list of available commands or see Chapter 3 for instructions on using the interactive solver 8 e To test the callable library the distribution includes sample codes along with associated proj
71. YMPHONY environment int numnodes OUT Pointer to an integer indicating the number of nodes analyzed so far Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 123 6 1 5 Data Modification Functions gt sym_set_obj_coeff double sym_set_obj_coeff sym_environment env int index double value Description This routine is used to set an objective coefficient Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN Index of the objective coefficient to be modified Note that it has to be at most the number of columns double value IN New objective value of the corresponding column Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 124 gt sym_set_obj2_coeff double sym_set_obj2_coeff sym_environment env int index double value Description This routine is used to set a coefficient of the second objective function of the corre sponding bicriteria problem Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int index IN Index of the objective coefficient to be modified Note that it has to be at most the number of columns double value IN New value of the objective coefficient to be modified Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMIN
72. YMPHONY uses other COIN libraries for certain functionality such as parsing MPS files solving linear programs generating valid inequalities and for general matrix utilities The libraries required for this functionality are now included with the distribution 2 2 1 4 GNU Libraries If the readline and history libraries are available the interactive solver will have command history and command completion available This functionality is only available in Unix like environments by configuring with the enable gnu packages option see below 2 2 2 Building in Unix like environments 2 2 2 1 Downloading You can obtain the SYMPHONY source code either via the subversion repository or in the form of archived releases The recommended method in Unix is to use subversion because it makes it easier to obtain updates In a Unix like environment such as Linux or CYGWIN the following command may be used to obtain SYMPHONY from source using SVN in most cases svn checkout https projects coin or org svn SYMPHONY stable 5 2 SYMPHONY 5 2 Alternatively you can get point releases of the source code as a compressed file by visiting 10 https www coin or org download source SYMPHONY The latest version of the source code is 5 2 3 so you should download the file SYMPHONY 5 2 3 tgz and unpack the distribution with tar xzf SYMPHONY 5 2 3 tgz This will create a subdirectory called SYMPHONY 5 2 3 containing the distribution 2 2 2 2 Config
73. _THIS_CHILD the child will be kept at this LP for further processing i e the process dives into that child PRUNE_THIS_CHILD the child will be pruned based on some problem specific property no questions asked PRUNE_THIS_CHILD FATHOMABLE the child will be pruned based on its pre solved LP relaxation and RETURN_THIS_CHILD the child will be sent back to tree manager Note that at most one child can be kept at the current LP module There are two default options in both of them objective values of the pre solved LP relaxations are compared for those children whose pre solve did not terminate with primal infeasibility or high cost One rule prefers the child with the lowest objective function value and the other prefers the child with the higher objective function value Arguments void user IN int ub IN branch_obj can IN char action OUT Return values USER_ERROR USER_SUCCESS USER DEFAULT PREFER_HIGHER_OBJ_VALUE PREFER_LOWER_OBJ_VALUE PREFER_MORE_FRACTIONAL PREFER_LESS_ FRACTIONAL Post processing Pointer to the user defined LP data structure The current best upper bound The branching candidate Array of actions for the children The array is already allocated to length can gt number Error DEFAULT is used User filled out action Regulated by the select_child default parameter which is initially set to PREFER_LOWER_OBJ_VALUE un less overridden by the user Choose child with the highest
74. _cut_num_per_iter 226 max_non_dual_feas_to_add_frac 225 max_non_dual_feas_to_add_min 225 max _not_fixable_to_add frac 225 max_not_fixable_to_add_max 225 max_presolve_iter 227 mc_find_supported_solutions 228 mc_rho 228 no_cut_timeout 226 not_fixed_storage_size 224 problem_type 224 select_candidates_default 227 select_child_default 228 send_feasible_solution_default 227 send lp solution default 227 set_obj_upper lim 224 shall _we_branch_ default 227 strong _branching_cand_num_max 227 strong branching cand_num min 227 strong branching red ratio 227 tailoff_gap backsteps 225 tailoff_gap_frac 225 tailoff_obj_backsteps 225 tailoff_obj_frac 225 try_to_recover_from_error 224 Master Module Parameters dg_debug 219 dg_exe 219 do_branch_and_cut 219 do_draw_graph 219 lower_bound 218 M_random seed 218 M_verbosity 218 mc_binary search tolerance 219 mc_ compare solution tolerance 219 mc_search_order 219 mc_warm_start 219 tm_debug 219 tm_exe 219 tm_machine 219 trim_warm_tree 219 upper_bound 218 upper_ bound estimate 219 Tree Manager Parameters cg debug 220 cg_exe 220 cg mach_num 220 colgen in first_phase 223 colgen_in_ second_phase 223 cp_debug 220 cp_exe 220 cp_mach_num 220 diving k 221 diving strategy 221 diving_threshold 221 find first feasible 224 fractional_diving num 221 fractional diving ratio 221 gap limit 224 keep_description_of_pruned 221 ke
75. _rhs 105 sym_get_row_activity 118 sym_get_row_lower 107 sym_get_row_range 106 sym_get_row_sense 104 sym_get_row_upper 108 sym_get_status 92 sym get_str param 91 sym_get_ub_for_new_obj 148 sym_get_ub_for_new_rhs 146 sym_get_user_data 83 sym_get_warm_start 142 sym_is_abandoned 97 sym_is_binary 114 sym_is_continuous 113 sym_is_integer 115 sym_is_iteration_limit_reached 95 sym_is_proven_optimal 93 sym_is_proven_primal infeasible 94 sym_is_target_gap_achieved 97 sym_is time limit_reached 96 sym_load_problem 74 sym_mc_solve 80 sym_open environmet 70 sym_parse_command_line 72 sym_read_gmpl 77 sym_read_mps 76 sym_read_warm_start 140 sym_set_col_lower 124 sym_set_col_names 134 sym_set_col_solution 130 sym_set_col_upper 125 sym_set_continuous 132 sym_set_dbl_param 87 sym_set_defaults 85 sym_set_int_param 86 sym_set_integer 133 sym_set_obj2_coeff 123 sym_set_obj_coeff 122 sym_set_obj_sense 129 sym_set_primal_bound 131 sym_set_row_lower 126 sym_set_row_type 128 sym_set_row_upper 127 sym_set_str_param 88 sym_set_user_data 82 241 sym_set_warm_start 143 sym_solve 78 sym_warm_solve 79 sym_write_warm_start_desc 139 242 Index of User Callback API Functions user_add_to_desc 189 user_check_cut 211 user_check validity_of cut 206 user_compare_candidates 185 user_create subproblem 174 user_dg free_window 216 user_dg_init_window 215 user_dg process_message 214 user_di
76. actional solutions with or without edge weights and node labels and weights as well as other information Furthermore the user can interactively modify the graph by for instance moving the nodes apart to disentangle the edges The user can also interactively enter violated cuts through the IGD interface To use IGD you must have installed PVM since the drawing window runs as a separate applica tion and communicates with the user s routines through message passing To compile the graph drawing application type make dg in the SYMPHONY root directory The user routines in the file user_dg c can be filled in but it is not necessary to fill anything in for basic applications After compiling dg the user must write some subroutines that communicate with dg and cause the graph to be drawn Regrettably this is currently a little more complicated than it needs to be and 63 is not well documented However by looking at the sample application it should be possible to see how it is done To enable graph drawing put the line do_draw_graph 1 into the parameter file or use the d command line option It can be difficult to get IGD to work If you are interested in using it and cannot get it to work feel free to contact me 5 6 5 Other Debugging Techniques Another useful built in function is write_mps which will write the current LP relaxation to a file in MPS format This file can then be read into the LP solver interactively or examined
77. age if this flag is set to true otherwise will own them Return values ERROR__USER Error User error detected in user_initialize_root_node function FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully TT gt sym_read_mps int sym_read_mps sym_environment env char infile Description This routine is used to load an instance from an MPS file Arguments sym_ environment env IN Pointer to the SYMPHONY environment char infile IN Pointer to a character array indicating the name of the file FUNCTION TERMINATED ABNORMALLY Function invoked unsuccessfully t 1 a Return values OTTON TERMINATED NORMALLY Function invoked successfully 78 gt sym_read_gmpl int sym_read_gmpl sym_environment env char modelfile char datafile Description This routine is used to load an instance from a GMPL file Arguments sym_ environment env IN Pointer to the SYMPHONY environment char modelfile IN Pointer to a character array indicating the name of the model file char datafile IN Pointer to a character array indicating the name of the data file FUNCTION TERMINATED ABNORMALLY Function invoked unsuccessfully t 1 oe Return values FONCTION TERMINATED NORMALLY Function invoked successfully 79 gt sym_solve int sym_solve sym_environment env Description This routine solves the currently loaded MILP problem from scratch even in the presen
78. aintaining the LP relaxations are similar to those of CPLEX and matrices are stored in the standard column ordered format 4 4 2 2 Managing the LP Relaxation The majority of the computational effort of BCP is spent solving LPs and hence a major emphasis in the development was to make this process as efficient as possible Besides using a good LP engine the primary way in which this is done is by controlling the size of each relaxation both in terms of number of active variables and number of active constraints The number of constraints is controlled through use of a local pool and through purging of ineffective constraints When a cut is generated by the cut generator it is first sent to the local cut pool In each iteration up to a specified number of the strongest cuts measured by degree of violation from the local pool are added to the problem Cuts that are not strong enough to be added to the relaxation are eventually purged from the list In addition cuts are purged from the LP itself when they have been deemed ineffective for more than a specified number of iterations where ineffective is defined as either 1 the corresponding slack variable is positive 2 the corresponding slack variable is basic or 3 the dual value corresponding to the row is zero or very small Cuts that have remained effective in the LP for a specified number of iterations are sent to the global pool where they can be used in later search nodes Cuts that have
79. alue Description This routine is used to learn whether the queried variable is continuous Arguments sym_environment env IN Pointer to the SYMPHONY environment int index IN The index of the queried variable Note that it has to be at most the number of columns int value OUT Pointer to a boolean indicating the variable status Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 115 gt sym_is_binary int sym_is_binary sym_environment env int index int value Description This routine is used to learn whether the queried variable is binary Arguments sym_environment env IN Pointer to the SYMPHONY environment int index IN The index of the queried variable Note that it has to be at most the number of columns int value OUT Pointer to a boolean indicating the variable status Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 116 gt sym_is_integer int sym_is_integer sym_environment env int index int value Description This routine is used to ask whether the queried variable is integer Arguments sym_environment env IN Pointer to the SYMPHONY environment int index IN Index of the queried variable Note that it has to be at most the number of columns int value OUT Pointer to a boolean indicating the variable status R
80. alue and upper bound 205 gt user_find_cuts int user_find_cuts void user int varnum int iter_num int level int index double objval int indices double values double ub double lpetol int cutnum Description In this function the user can generate cuts based on the current LP solution stored in soln Cuts found are added to the LP by calling the cg_add_user_cut cut_data new_cut function which then transfers the cut to the LP module either through message passing or shared memory The argument of this function is a pointer to the cut to be sent See Section 6 3 2 for a description of this data structure Each user defined cut assigned a type and a designated packed form Each user defined type must be recognized by the user s user_unpack_cuts 6 3 2 2 function in the master module If the user wants a user defined cut to be added to the cut pool in case it proves to be effective in the LP then new_cut gt name should be set to CUT__SEND_TO_CP In this case the cut must be globally valid Otherwise it should be set to CUT__DO_NOT_SEND_TO_CP Alternatively SYMPHONY provides automated support for the generation of cuts represented explicitly as matrix rows These cuts are passed as sparse vectors and can be added by calling the routine cg_add_explicit_cut which has the following interface int cg_add_explicit_cut int nzcnt int indices double values double rhs double range char sense char send_to_cp Here n
81. ance if a change is made prep_impl dive_level integer 0 In some advanced preprocessing routines a variable or con straint is modified to check what implications can be derived from that change When such an implication is derived it can recursively lead to more implications This parameter controls how many levels of recursion are allowed prep impl1_1imit integer 50 Determines the maximum number of implications that can be derived from preprocessing prep do probing integer 1 Determines if probing is used while preprocessing Probing is not yet implemented and this parameter does not have any effect prep _verbosity integer 1 Determines the verbosity of messages from the preprocessing stage Higher levels will produce more verbose messages prep_reduce_mip boolean 1 If some variables and constraints have been eliminated in pre processing and if prep_reduce_mip is 1 then the memory allocated for these deleted variables and constraints is freed Otherwise these are retained in the instance but are never used prep_probing verbosity integer 0 Determines the verbosity of messages from probing stage Probing is not yet implemented and this parameter does not have any effect prep probing level integer 1 Determines the maximum level of probing that is carried out before preprocessing is stopped Probing is not yet implemented and this parameter does not have any effect prep_display_stats boo
82. and cut Since then many implementations including ours have been fashioned around the framework they described for solving the Traveling Salesman Problem As an example let a combinatorial optimization problem CP E F with ground set E and feasible set F C 2 be given along with a cost function c R The incidence vectors corresponding to the members of F are sometimes specified as the the set of all incidence vectors obeying a relatively small set of inequalities These inequalities are typically the ones used in the initial LP relaxation Now let P be the convex hull of incidence vectors of members of F Then we know by Weyl s Theorem see 28 that there exists a finite set of inequalities valid for P such that P lrER ar lt 8 V a 8 L 4 1 The inequalities in are the potential cutting planes to be added to the relaxation as needed Unfortunately it is usually difficult if not impossible to enumerate all of inequalities in or we could simply solve the problem using linear programming Instead they are defined implicitly and we use separation algorithms and heuristics to generate these inequalities when they are violated In Figure 4 1 we describe more precisely how the bounding operation is carried out in branch and cut Once we have failed to either prune the current subproblem or separate the current fractional solution from P we are forced to branch The branching operation is accomplished by specifyin
83. ant to compile only one of them type devenv xxx sln make all debug or devenv xxx sln make all release For each command the executable user will be created in Debug and or Release directo ries To test the executable type Debug xxx exe F sample xxx If the source files for the application are modified type devenv user sln make all rebuild in order to clean and rebuild everything 18 2 2 4 3 Using the NMAKE Utility e Edit the file xxx mak where xxx is the name of the application to reflect your environment Only minor edits should be required An explanation of what has to be set is contained in the comments in the xxx mak file Also see the related parts of Section 2 2 3 4 above e Once configuration is done type nmake f xxx mak The executable xxx exe will be created in the Debug directory e To test the executable type Debug xxx exe F sample xxx 19 enable debug enable debug symphony enable doscompile enable static enable static executable enable gnu packages compile all projects with debug options set compile only SYMPHONY project with debug options Under Cygwin compile so that executables do not depend on the CYGWI build static libraries create a complete static executable compile with GNU packages compile interactive optimizer with readline library disable cgl cuts enable sensitivity analysis enable root only enable frac branching
84. arded USER_SUCCESS User unpacked the cuts USER_DEFAULT There are no user cut types defined In this case SYMPHONY deals with only explicit cuts and internally generated cuts Wrapper invoked from Wherever a cut needs to be unpacked multiple places Post processing Explicit row cuts are processed as well as SYMPHONY s internally generated cuts Also the pointers to each cut are transferred to the waiting_rows data structure in previous version this was done by the user 193 Notes When decoding the cuts the expanded constraints have to be adjusted to the current LP i e coefficients corresponding to variables currently not in the LP have to be left out If the one_row_only flag is set to UNPACK_CUTS_MULTIPLE then the user can generate as many constraints even zero from a cut as she wants this way she can lift the cuts thus adjusting them for the current LP However if the flag is set to UNPACK_CUTS_SINGLE then for each cut the user must generate a unique row the same one that had been generated from the cut before The flag is set to this value only when regenerating a search tree node The from argument can take on six different values CUT_FROM_CG CUT_FROM_CP CUT_FROM_TM CUT_LEFTOVER these are cuts from a previous LP relaxation that are still in the local pool CUT_NOT_IN_MATRIX_SLACK and CUT_VIOLATED_SLACK indicat ing where the cut came from This might be useful in deciding whether to lift the cu
85. as function stubs for developing a custom application using SYMPHONY s callbacks The subdirectory SYMPHONY Applications USER contains the files needed for implementing the call backs and is a template for developing an application In this directory and its subdirectories which mirror the subdirectories of SYMPHONY itself each file contains function stubs that can be filled in to create a new custom application There is a separate subdirectory for each module master Master tree management TreeManager cut generation CutGen cut management CutPool and node processing LP Within each subdirectory there is a file initially called USER user_ c where is the name of the module The primary thing that you as the user need to understand to build a custom application is how to fill in these stubs That is what the second part of this chapter is about Before describing that however we will discuss how to build your application 57 5 2 Building an Application Note that the template application can be built and will work without modifying any of the source files In this case SYMPHONY will behave according to default settings 5 2 1 Unix First download configure and compile SYMPHONY as described in Section 2 2 2 5 This will generate the required library and makefiles for each application After this typing make in the SYMPHONY Applications USER subdirectory should successfully build the executable For more inf
86. ase without doing any column generation yet If only the second to last bit is set then we do column generation immediately and resolve if any new columns are found The next two higher bits determine whether or not to do column generation before branch ing If only the third lowest bit is set then no column generation occurs before branching If only the fourth lowest bit is set then column generation is attempted before branching The default is not to generate columns before branching or fathoming which corresponds to only the third lowest bit being set resulting in a default value of 4 time _1imit double 1 0 Number of seconds of wall clock time allowed for solution When this time limit is reached the solution process will stop and the best solution found to that point along with other relevant data will be output A time limit less than 0 0 means there is no limit node_limit integer 1 Number of nodes allowed to be analyzed during the solution When this node limit is reached the solution process will stop and the best solution found to that point along with other relevant data will be output A node limit less than 0 means there is no limit gap 1imit double 1 0 Target gap limit allowed for solution When the gap between the lower and the upper bound reaches this point the solution process will stop and the best solution found to that point along with other relevant data will be output A gap limit less t
87. ay violate an inequality not present in the current relaxation that will be generated by the user during the cut generation phase In such a case it is not possible for SYMPHONY to determine this and the user should set feasible to IP_INFEASIBLE and return USER_SUCCESS If the user tests the feasibility status of the solution and determines that the current solution is feasible then feasible should be set to IP_FEASIBLE instead IN this case the user can set objval to the true objective function value of the feasible solution to account for any round ff error introduced by the solver if desired If all integral solution must be feasible the user can ask SYMPHONY to simply test the integrality status by returning USER_DEFAULT If the user is able to generate a feasible solution using a primal heuristic then the solution can be returned in a dense format in the array heur_solution In this case objval should be set to the value of the solution and feasible should be set to IP_HEUR_FEASIBLE Arguments void user INOUT Pointer to the user defined LP data structure double lpetol IN The e tolerance of the LP solver int varnum IN The length of the indices and values arrays int indices IN User indices of variables at nonzero level in the current solution double values IN Values of the variables listed in indices int feasible OUT Feasibility status of the solution IP_INFEASIBLE IP_FEASIBLE or IP_HEUR FEASIBLE double obj
88. bsets is called a subproblem S1 Sn are sometimes called the children of S We add the children of S to the list of candidate subproblems those which need processing This is called branching To continue the algorithm we select one of the candidate subproblems and process it There are four possible results If we find a feasible solution better than then we replace with the new solution and continue We may also find that the subproblem has no solutions in which case we discard or prune it Otherwise we compare the lower bound to our global upper bound If it is greater than or equal to our current upper bound then we may again prune the subproblem Finally if we cannot prune the subproblem we are forced to branch and add the children of this subproblem to the list of active candidates We continue in this way until the list of active subproblems is empty at which point our current best solution is the optimal one 4 2 Branch and Cut In many applications the bounding operation is accomplished using the tools of linear programming LP a technique first described in full generality by Hoffman and Padberg 20 This general class 35 Bounding Operation Input A subproblem S described in terms of a small set of inequalities such that S x s F and ax lt BV a 6 L and a an upper bound on the global optimal value Output Either 1 an optimal solution s S to the subproblem 2 a low
89. ccessfully gt sym_create_permanent_cut_pools int sym_create_permanent_cut_pools sym_environment env int cp_num Description This routine is used to create a global cut pool that will be saved even after the solve call exits and can be used to initialize the cut pool for later solve calls This can be useful when solving a series of related MILPs that share classes of globally valid inequalities For instance if only the objective function is varied as is the case with multicriteria integer programming then cuts can be saved for use in later solve calls Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int cp num OUT Pointer to an integer indicating the number of cut pools stored in the environment Return values INT The number of the cut pools created 83 gt sym_set_user_data int sym_set_user_data sym_environment env void user Description This routine is used to give SYMPHONY a pointer to the user s problem data structure This pointer will then be handed back to the user during subsequent calls to user call backs This allows the user to store static problem data Note that this pointer can also be stored by filling out the callback function user_initialize see Section 6 3 1 Arguments sym_ environment env INOUT Pointer to the SYMPHONY environment void user IN Pointer to the user defined problem structure Return values ERROR__USER Error in the passed in user structure FUN
90. ce of a loaded warm start Any warm start information loaded or kept before will be deleted from the environment Arguments sym_environment env INOUT Pointer to the SYMPHONY environment Return values ERROR__USER TM_OPTIMAL_SOLUTION_FOUND TM_TIME_LIMIT_EXCEEDED TM_NODE_LIMIT_EXCEEDED TM_TARGET_GAP_ACHIEVED TM_FOUND_FIRST_FEASIBLE TM_ERROR__NO_BRANCHING_CANDIDATE TM_ERROR__ILLEGAL_RETURN_CODE TM_ERROR__ NUMERICAL_INSTABILITY TM_ERROR__ COMM_ERROR TM_ERROR__USER 80 Error User error detected in one of user_send lp data user_send_cg_data user_send_cp_data user_receive_feasible_solution user display _solution and user_process_own_messages functions Tree Manager TM found the optimal solution and stopped TM stopped after reaching the predefined time limit TM stopped after reaching the predefined node limit TM stopped after achieving the predefined target gap TM stopped after finding the first feasible solution Error TM stopped User didn t select branching candidate in user_select_candidates callback Error TM stopped after getting a non valid return code Error TM stopped due to some numerical difficul ties Error TM stopped due to communication error Error TM stopped User error detected in one of user callbacks called during TM processes gt sym_warm_solve int sym_warm_solve sym_environment env Description This routine re solves the corresponding problem a
91. ceeds the given maximum or is less than the given minimum in which case it is set to the max or min respectively max_not_fixable_to_add_min max _not_fixable to add max max_not_fixable_to_add_frac integer integer double 100 500 1 As above these three parameters determine the maximum number of new columns to be added to the problem because they cannot be priced out These variables are only added when trying to restore infeasibility and usually this does not require many variables anyway mat_col_compress_num mat_col_compress_ratio integer double 50 05 Determines when the matrix should be physically compressed This only happens when the number of columns is high enough to make it worthwhile The matrix is physically compressed when the number of deleted columns exceeds either an absolute number and a specified fraction of the current number of active columns mat_row_compress_num mat_row_compress_ratio integer double 20 05 Same as above except for rows 228 tailoff gap backsteps tailoff gap frac integer double 2 99 Determines when tai loff is detected in the LP module Tailoff is reported if the average ratio of the current gap to the previous iteration s gap over the last tailoff_ gap backsteps iterations wasn t at least tailoff gap frac tailoff_obj_backsteps tailoff_obj_frac integer double 2 99 Same as above only the ratio is taken with respect to the change in object
92. ch and bound include PUBB 33 BoB 4 PPBB Lib 35 and PICO 10 PARINO 23 and FATCOP 6 are parallel MILP solvers 1 5 How to Use This Manual The manual is divided into six chapters The first is the introduction which you are reading now Chapter 2 describes how to install SYMPHONY from either a source or binary distribution If you have already managed to get SYMPHONY running using the instructions in the README file you might want to skip to Chapter 3 However keep in mind that the manual contains additional details for customizing your build Chapter 3 contains an overview of how to use in all three major modes as a black box solver through the interactive shell or on the command line as a callable library and as a customizable framework Chapter 4 contains further depth and a more complete technical description of the design and implementation of SYMPHONY In Section 4 3 we describe the overall design of SYMPHONY without reference to the implementational details and with only passing reference to parallelism In Section 4 4 we discuss the details of the implementation In Section 4 5 we briefly discuss issues involved in parallel execution of SYMPHONY Chapter 5 describes in detail how to develop a custom application using SYMPHONY Note that it is not necessary to read Chapter 4 before undertaking development of a SYMPHONY application but it may help Chapter 6 contains reference material Section 6 1 contains a description of th
93. ch of the children These are defined and used exactly as in the cut_data data structure Note If a limit is defined on the number of children by defining the MAX_CHILDREN_NUM macro to be a number it is pre defined to be 4 as a default then these arrays will be statically defined to be the correct length and don t have to be allocated This option is highly recommended Otherwise the user must allocate them to be of length child_num double lhs The activity level for the row for branching cuts This field is purely for the user s convenience SYMPHONY doesn t use it so it need not be filled out double objval int termcode int iterd int feasible The objective values termination codes number of iterations and feasibility stati of the children after pre solving them These are all filed out by SYMPHONY during strong branching The user may access them in user_compare_candidates see below There are three default options see below each chooses a few variables the number is determined by the strong branching parameters see Section 6 4 5 Arguments Same as for user_shall_we_branch except that action must be either USER__DO_BRANCH or USER__DO_NOT_BRANCH and if branching is asked for there must be a real candidate in the candidate list not only VIOLATED SLACKs and SLACK_TO_BE_DISCARDEDs Also the argument bc_level is the level in the tree This could be used in deciding how many strong branching candidates to use R
94. concert and communicate with each other through shared memory instead of across the network When running as separate process the modules communicate with each other using a standard communications protocol Currently the only option supported is PVM but it would be relatively easy to add an MPI implementation 45 The overall flow of the algorithm is similar to other branch and bound implementations and is detailed below A priority queue of candidate subproblems available for processing is maintained at all times and the candidates are processed in an order determined by the search strategy The algorithm terminates when the queue is empty or when another specified condition is satisfied A new feature in SYMPHONY 5 2 3 is the ability to stop the computation based on exceeding a given time limit exceeding a given limit on the number of processed nodes achieving a target percentage gap between the upper and lower bounds or finding the first feasible solution After halting prematurely the computation can be restarted after modifying parameters or problem data This enables the implementation of a wide range of dynamic and on line solution algorithms as we describe next 4 4 1 2 Solve from Warm Start Among the utility classes in the COIN OR repository is a base class for describing the data needed to warm start the solution process for a particular solver or class of solvers To support this option for SYMPHONY we have implemented such a warm
95. ctories To install in another directory use the option prefix DIR to the configure command After compilation the SYMPHONY library together with the header files in the subdirectory include can then be used to call SYMPHONY from any C C code The API for this is described in Chapter 3 The executable can also be used for solving generic MILP problems in MPS and GMPL format In order to read GMPL files you will need to have GLPK http www gnu org software glpk installed To install it automatically run the get G1pk script in the ThirdParty Glpk directory After that Glpk should be built and linked automatically enabling the ability to read GMPL files For a more powerful modeling interface FlopC can also be used to obtain a capability similar to ILOG s Concert technology for building math programming models see SYMPHONY Exam ples FLOPC To test SYMPHONY after building type make test to execute an automated unit test To test out the optimizer manually a sample MPS file called sample mps and a sample GMPL AMPL file called sample mod together with its data file sample dat are included with the distribution You can use either the command line or the interactive optimizer To solve the sample MPS model type bin symphony F SYMPHONY Datasets sample mps To solve the GMPL model use the F switch to specify the file name and the D for the data file name if the input is in GMPL AMPL format i e type bin symphony
96. customized solvers for the matching problem the set partitioning problem simple and advanced versions the vehicle routing and traveling salesman problems the mixed postman problem the multi criteria knapsack problem and the capacitated network routing problem These applications are contained in the SYMPHONY Applications subdi rectory in the distribution There is also a white paper that guides the user through the development of the MATCH solver in the SYMPHONY Doc directory For detailed instructions on developing your own application with SYMPHONY see Chapter 5 14 In order to compile SYMPHONY s applications in Unix like environments you must first compile a version of the callable library with hooks for the callback functions configure with application make make install This will create the application library called 1ibSymAppl1 to be used while building custom ap plications Note that that the generic sequential library and executable will also be made and installed After building the library go to one of the application subdirectories in the SYMPHONY Applications directory and type make there to build the corresponding application For more information includ ing the parallel configuration instructions see the INSTALL file of the corresponding application 2 2 3 Building Using Microsoft Visual C Here is a sketch outline of how to compile SYMPHONY in MS Windows with the MSVC compiler These instructions wil
97. d 2 index i matind 2 index 1 j ub index 1 0 index y matbeg n 2 n 66 set the initial right hand side for i 0 i lt m i rhs ai 1 sense i E Load the problem to SYMPHONY sym_explicit_load_problem env n m matbeg matind matval lb ub is_int obj 0 sense rhs rngval true return FUNCTION_TERMINATED_NORMALLY Now we are ready to gather everything in the main routine in user_main This will involve to create a SYMPHONY environment and a user data structure read in the data create the corresponding IP load it to the environment and ask SYMPHONY to solve it CALL_FUNCTION is just a macro to take care of the return values int main int argc char argv int termcode char infile Create a SYMPHONY environment sym_environment env sym_open_environment Create the data structure for storing the problem instance user_problem prob user_problem calloc 1 sizeof user_problem CALL_FUNCTION sym_set_user_data env void prob CALL_FUNCTION sym_parse_command_line env argc argv CALL_FUNCTION sym_get_str_param env infile_name amp infile CALL_FUNCTION match_read_data prob infile CALL_FUNCTION match_load_problem env prob CALL_FUNCTION sym_solve env CALL_FUNCTION sym_close_environment env return 0 OK that s it That defines an integer program and if you compile an
98. d by the objective vector Note that the size of this array has to be at least the number of columns Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 112 gt sym_get_obj2_coeff int sym_get_obj2_coeff sym_environment env double obj2 Description This routine is used to get the second objective vector if it exists By default it is set to the zero vector Arguments sym_environment env IN Pointer to the SYMPHONY environment double obj2 OUT Pointer to a double type array to be filled by the second objective vector Note that the size of this array has to be at least the number of columns Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 113 gt sym_get_obj_sense int sym_get_obj_sense sym_environment env int sense Description This routine is used to get the objective sense Arguments sym_environment env IN Pointer to the SYMPHONY environment int sense OUT Pointer to an integer indicating the objective sense In return it will be 1 in case of minimization and 1 in case of maximization Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 114 gt sym_is_continuous int sym_is_continuous sym_environment env int index int v
99. d on initial parameter settings and the ex ecution of the algorithm or else do nothing if execution of the subroutine is optional built_in option1 built_in option2 The specified built in option will be used Notes e Sometimes an output is optional This is always noted in the function descriptions e If an array has to be returned i e the argument is type array then unless other wise noted the user has to allocate space for the array itself and set array to be the array allocated If an output array is optional and the user is not returning any values in that array then the user must not set array because this is how SYMPHONY decides which optional arrays are filled up e Some built in options are implemented so that the user can invoke them directly from the callback function This might be useful if for example the user wants to use different built in options at different stages of the algorithm 5 4 Data Structures The user can define her own data structure for each module to maintain problem data and any other information the user needs access to in order to implement functions to customize the solver A pointer to this data structure is maintained by SYMPHONY and is passed to the user as an ar gument to each user function The pointer must be initially passed using the sym_set_user_data 60 command Since SYMPHONY knows nothing about this data structure it is up to the user to allocate it and maintain it T
100. d optimize it the rest of the system will come together to solve this problem Here is a data file to use 011333 67 101333 110333 333011 3 3 9 01 33 31 100 The optimal value is 5 To display the solution we need to be able to map back from variables to the nodes That was the use of the node1 and node2 parts of the USER PROBLEM We can now use user_display_solution in user_master c to print out the solution int user_display_solution void user double lpetol int varnum int indices double values double objval This gives you access to the user data structure user_problem prob user_problem user int index for index 0 index lt varnum index if values index gt lpetol printf 2d matched with 2d at cost 6d n prob gt nodel indices index prob gt node2 indices index prob gt cost prob gt node1 indices index prob gt node2 indices index J return USER_SUCCESS We will now update the code to include a crude cut generator Of course We could go for a Gomory Hu type odd set separation ala Gr tschel and Padberg but for the moment let s just check for sets of size three with more than value 1 among them such a set defines a cut that requires at least one edge out of any odd set We can do this by brute force checking of triples as follows int user_find_cuts void user int varnum int iter_num int level int index double objval int indices d
101. decode the compact representation of the cuts see the cut_data structure into rows for the matrix A pointer to the array of generated rows must be returned in new_rows the user has to allocate this array and their number in new_row_num Note that SYMPHONY has built in support for cuts generated explicitly as ma trix rows with no user defined packed form i e cuts whose indices and coefficients are given explicitly see the function user_find_cuts in Section 6 3 3 These cuts can be constructed and added using the helper function cg_add_explicit_cut see the description of user_find_cuts in Section 6 3 3 and are packed and unpacked automatically so the user does not need to implement this function In post processing SYMPHONY unpacks explicitly defined cuts and internally generated generic cuts Arguments void user IN Pointer to the user defined LP data structure int from IN See below in Notes int type IN UNPACK_CUTS_SINGLE or UNPACK_CUTS MULTIPLE see notes below int varnum IN The number of variables var_desc vars IN The variables currently in the problem int cutnum IN The number of cuts to be decoded cut_data cuts IN Cuts that need to be converted to rows for the current LP See Warning below int new_row_num OUT Pointer to the number of rows in new_rows waiting row new_rows OUT Pointer to the array of pointers to the new rows Return values USER_ERROR Error The cuts are disc
102. direction and if yes which one is stronger The default is to declare the cuts comparable only if the type sense and coef fields of the two cuts are the same byte by byte and if this is the case to compare the right hand sides to decide which cut is stronger Arguments void user IN Pointer to the user defined LP data structure cut_data cuti IN The first cut cut_data cut2 IN The second cut int same_cuts OUT Possible values SAME FIRST_CUT_BETTER SECOND_CUT_BETTER and DIFFERENT i e not com parable Return values USER_ERROR Error DEFAULT is used USER_SUCCESS User did the comparison filled out same_cuts USER DEFAULT Compare byte by byte see above Wrapper invoked from process message when a PACKED_CUT arrives Note This function is used to check whether a newly arrived cut is already in the local pool If so or if it is weaker than a cut in the local pool then the new cut is discarded if it is stronger then a cut in the local pool then the new cut replaces the old one and if the new is different from all the old ones then it is added to the local pool 192 gt user_unpack_cuts int user_unpack_cuts void user int from int type int varnun var_desc vars int cutnum cut_data cuts int new_row_num waiting_row new_rows Description If the user has defined application specific cut classes these cuts must be interpreted as constraints for the current LP relaxation i e the user must
103. double 1 7 Determines when reduced cost fixing should be attempted It is only done when the gap is within the fraction gap_as_ub_frac of the upper bound or when the gap has decreased by the fraction gap_as_last_gap_frac since the last time variables were fixed do logical fixing boolean FALSE Determines whether the user s logical fixing routine should be used fixed to ub before logical fixing fixed_to_ub_frac_before_logical_fixing integer double 1 01 Determines when logi cal fixing should be attempted It will be called only when a certain absolute number and a certain number of variables have been fixed to their upper bounds by reduced cost This is because it is typically only after fixing variables to their upper bound that other variables can be logically fixed max_presolve_iter integer 10 Number of simplex iterations to be performed in the pre solve for strong branching strong branching_cand_num_max strong _branching_cand_num_min strong branching red ratio integer 10 5 1 These three parameters together determine the number of strong branching candidates to be used by default In the root node strong branching cand num max candidates are used On each succeeding level this number is reduced by the number strong_branching_red_ratio multiplied by the square of the level This continues until the number of candidates is reduced to strong_branching_cand_num_min and then that number of candidate
104. e 4 5 2 Inter process Communication SYMPHONY can utilize any third party communication protocol supporting basic message passing functions All communication subroutines interface with SYMPHONY through a separate commu nications APT Currently PVM 15 is the only message passing protocol supported but interfacing with another protocol is a straightforward exercise Additionally it is possible to configure the code to run in parallel using threading to process multiple search tree nodes simultaneously Currently this is implemented using OpenMP compiler directives 55 to specify the parallel regions of the code and perform memory locking functions Compiling the code with an OpenMP compliant compiler will result in a shared memory parallel executable For a list of OpenMP compliant compilers and other resources visit http www openmp org 4 5 3 Fault Tolerance Fault tolerance is an important consideration for solving large problems on computing networks whose nodes may fail unpredictably The tree manager tracks the status of all processes and can restart them as necessary Since the state of the entire tree is known at all times the most that will be lost if an NP module or cut generator is killed is the work that had been completed on that particular search node To protect against the tree manager itself or a cut pool being killed full logging capabilities have been implemented If desired the tree manager can write out the entir
105. e before resolving This consists of locating nodes whose descendants are all likely to be pruned in the resolve and eliminating those descendants in favor of processing the parent node itself This ability could be extended to allow changes that invalidate the warm start data of some leaf nodes The ability to resolve after modifying problem data has a wide range of applications in practice One obvious use is to allow dynamic modification of problem data during the solve procedure or even after the procedure has been completed Implementing such a solver is simply a matter of periodically stopping to check for user input describing a change to the problem Another obvious application is in situations where it is known a priori that the user will be solving a sequence of very similar MILPs This occurs for instance when implementing algorithms for multicriteria optimization as we describe in Section 4 4 1 3 One approach to this is to solve a given base problem possibly limiting the size of the warm start tree save the warm start information from the base problem and then start each subsequent call from this same checkpoint Code for implementing this was shown in Figure 3 3 4 4 1 3 Bicriteria Solve For those readers not familiar with bicriteria integer programming we briefly review the basic notions here For clarity we restrict the discussion here to pure integer programs ILPs but the principles are easily generalized A bicrite
106. e state of the tree to disk periodically allowing a warm restart if a fault occurs Similarly the cut pool process can be warm started from a log file This not only allows for fault tolerance but also for full reconfiguration in the middle of solving a long running problem Such reconfiguration could consist of anything from adding more processors to moving the entire solution process to another network 56 Chapter 5 Developing Custom Applications 5 1 Navigating the Source Code To develop an application with SYMPHONY you need to first understand how the source files are organized Note that in this chapter all path names are given Unix style When you unpack the SYMPHONY source distribution you will notice at the root level a number of files associ ated with the automatic configuration system as well as a number of subdirectories each of which corresponds to a library used by SYMPHONY for some specific functionality The files associated with SYMPHONY itself are located in the SYMPHONY subdirectory Within the SYMPHONY sub directory are a number of other subdirectories including one called src containing the source files for SYMPHONY itself Also in the main SYMPHONY subdirectory there is a subdirectory called Applications see Sec tions 2 2 2 5 and 2 2 4 for instructions on building the applications The Applications subdi rectory contains the source code for a number of sample applications developed with SYMPHONY as well
107. e ub The upper bounds of the variables char colname The column names 171 gt cut_data Another of the internally defined data structures that the user has to deal with frequently is the cut_data data structure used to store the packed form of cuts This structure has 8 fields listed below int size The size of the coef array char coef An array containing the packed form of the cut which is defined and con structed by the user Given this packed form and a list of the variables active in the current relaxation the user must be able to construct the corresponding constraint double rhs The right hand side of the constraint double range The range of the constraint It is zero for a standard form constraint Otherwise the row activity level is limited to between rhs and rhs range char type A user defined type identifier that represents the general class that the cut belongs to char sense The sense of the constraint Can be either L lt E G gt or R ranged This may be evident from the type char deletable Determines whether or not a cut can be deleted once added to the for mulation TRUE by default char branch Determines whether the cut can be branched on or not Possible initial values are DO_NOT_BRANCH_ON_THIS_ROW and ALLOWED_TO_BRANCH_ON int name Identifier used by SYMPHONY The user should not set this 172 gt waiting_
108. e VBC TOOL emulation mode If one of these modes is chosen then the tree will be displayed in real time using the VBCTOOL Software When using the option VBC_EMULATION_LIVE 2 and piping the output directly to VBCTOOL the tree will be displayed as it is constructed with color coding indicating the status of each node With VBC_EMULATION_FILE 1 selected a log file will be produced which can later be read into VBCTOOL to produce an emulation of the solution process at any desired speed If VBC_EMULATION_FILE is selected the the follow ing line should contain the keyword vbc_emulation_file_name along with the corresponding file name for a value price_in_root boolean FALSE Whether to price out variables in the root node before the second phase starts called repricing the root trim_search_tree boolean FALSE Whether to trim the search tree before the second phase starts or not Useful only if there are two phases It is very useful then 226 colgen_in first_phase colgen_in_ second phase integers both 4 These parameters de termine if and when to do column generation in the first and second phase of the algorithm The value of each parameter is obtained by setting the last four bits The last two bits refer to what to do when attempting to prune a node If neither of the last two bits are set then we don t do anything we just prune it If only the last bit is set then we simply save the node for the second ph
109. e is commonly referred to as strong branching When the branching object has been selected the NP module sends a description of that object to the tree manager which then creates the children and adds them to the list of candidate nodes It is then up to the tree manager to specify which node the now idle NP module should process next This issue is further discussed below 51 4 4 3 The Tree Management Module 4 4 3 1 Managing the Search Tree The tree managers primary job is to control the execution of the algorithm by deciding which candidate node should be chosen as the next to be processed This is done using either one of several built in rules or a user defined rule Usually the goal of the search strategy is to minimize overall running time but it is sometimes also important to find good feasible solutions early in the search process In general there are two ways to decrease running time either by decreasing the size of the search tree or by decreasing the time needed to process each search tree node To minimize the size of the search tree the strategy is to select consistently that candidate node with the smallest associated lower bound In theory this strategy sometimes called best first will lead the smallest possible search tree However we need to consider the time required to process each search tree node as well This is affected by both the quality of the current upper bound and by such factors as communication overhead and
110. e native C interface for the callable library Section 6 2 contains a description of the interface for C environments Section 6 3 contains a description of the user callback functions SYMPHONY s parameters are described in Section 6 4 For reference use the HTML version of this manual may 4 be more practical as the embedded hyperlinks make it easier to navigate 1 6 Getting Additional Help The main point of entry for additional help trouble shooting and problem solving is the SYM PHONY Wiki and development Web site at https projects coin or org SYMPHONY There bug reports can be submitted by clicking on the New Ticket button and also previous bug reports searched For general questions there is also a SYMPHONY user s mailing list To subscribe visit http list coin or org mailman listinfo coin symphony Chapter 2 Installing SYMPHONY This chapter is concerned with detailed instructions for building and installing SYMPHONY along with its associated libraries and applications 2 1 Installing the Binary Distribution For the users who only need the generic MILP solver or the SYMPHONY callable library to be used in their custom applications there are binary distributions released for different compilers and platforms Each distribution consists of an executable and libraries built from the source code of SYMPHONY version 5 2 3 It is important to note however that the pre compiled binaries are missing some ver
111. e uaa a aa a a ea a a S11 Unix like Environments s sek saesae kee a a e a a 31 2 Microsoft Windows 2 lt 5 0000000 aue a a eS a a h ew we ZLo Mon Ment sayi a a a oe eae a Das ws Gems ge Be e E Gg h aE TR ee g 10 10 15 17 She Sa Menmi sore iaa Piek a ulie dle ee a bo BSS ES SSS bos eS 24 ole Depay Me coccion dad ea be bbe Rhee Re ks 25 SLG Sub Mena Browse 12 54 08 be ete eee ee eR ee ee ee 26 3 2 Using SYMPHONY from the Command Line 2 27 3 0 Using the Callable Library neee aca iia m fa ea ee Be RRR ee we ee a 28 Geol The CAPIL os does de ae eg ee a RG eS See a ae 28 Saa The UF AP 2 ac ce ee ee we ee Be a ee eed 31 3 3 3 Linking to the Callable Library soe s es ce o e 32 34 Usne the Callback Funcions s soore 26 68 ee es ee ee ee 33 4 Technical Details 35 Al Branch and Bouni s osas ee Ee ee hae amp ee ee eo 35 Al Brinck BH ROM aoe a eek oh ee ew ee eae BR a ee Eh ee ee E 35 43 Design of SYMPHONY 2 2 2 5 ocio aa ee a es 37 4 3 1 An Object oriented Approach 0 0000002 eee 38 4 3 2 Data Structures and Storage lt o oso sse topuri aaar mupp 39 4 3 3 Modular Implementation s c roosa omen Re ee 41 43d Algorithm CUA sobr ag a a RR AA ge a 44 4 4 Details of the Implementation e 45 4 4 1 The Master Module e 45 4 42 The Node Processing Module 202 00020000 48 44 3 The Tree Management Module os si 045 4004 ee RE BR ee Re es 52 4 44
112. e variables In order to avoid pricing variables in every leaf of the tree we can trim the tree before the start of the second phase Trimming the tree consists of eliminating the children of any node for which each child has lower bound above the current upper bound We then reprocess the parent node itself This is typically more efficient since there is a high probability that given the new upper bound and cuts we will be able to prune the parent node and avoid the task of processing each child individually 4 4 4 The Cut Generation Module To implement the cut generator process the user must provide a function that accepts an LP solution and returns cuts violated by that solution to the NP module In parallel configurations each cut is returned immediately to the NP module rather than being passed back as a group once the function exits This allows the LP to begin adding cuts and solving the current relaxation before the cut generator is finished if desired Parameters controlling if and when the LP should begin solving the relaxation before the cut generator is finished can be set by the user SYMPHONY now generates generic cutting planes using the Cut Generator Library also available from COIN COIN https projects coin or org Cgl The CGL can be used to generate cuts in cases where problem specific cutting planes are not available or not implemented yet 4 4 5 The Cut Management Module 4 4 5 1 Maintaining and Scanning the Pool
113. easibility pump keeps generating solutions that have high objective function values When the number of such solutions becomes more than fp poor sol lim fac times the number of good solutions the pump is disabled 6 4 6 Cut Generator Parameters CG_verbosity integer 0 Verbosity level for the cut generator module 6 4 7 Cut Pool Parameters CP_verbosity integer 0 Verbosity of the cut pool module 233 cp_logging boolean 0 Determines whether the logging option is enabled In this case the entire contents of the cut pool are written out periodically to disk at the same interval as the tree manager log files are written If this option is set then the line following must start with the keyword cp_log_file_name and include the appropriate file name as the value cp_warm_start boolean 0 Used to allow the cut pool to make a warm start by reading in a previously written log file If this option is set then the line following must start with the keyword cp_warm_start_file_name and include the appropriate file name as the value block_size integer 5000 Indicates the size of the blocks to allocate when more space is needed in the cut list max_size integer 2000000 Indicates the maximum size of the cut pool in bytes This is the total memory taken up by the cut list including all data structures and the array of pointers itself max_number_of_cuts integer 10000 Indicates the maximum number of
114. eate a minimum matching on a complete graph Initially we will just formulate this as an integer program with one variable for each possible pair that can be matched Then we will include a set of constraints that can be added by cut generation We begin with the template code in the USER subdirectory included with SYMPHONY This gives stubs for each user callback routine First I need to define a data structure for describing an instance of the matching problem We use the template structure USER_PROBLEM in the file include user h for this purpose To describe an instance we just need the number of nodes and the cost matrix In addition we also need a way of assigning an index to each possible assignment Here is the data structure typedef struct USER_PROBLEM int numnodes 64 int cost MAXNODES MAXNODES int match1 MAXNODES MAXNODES 1 2 int match2 MAXNODES MAXNODES 1 2 int index MAXNODES MAXNODES user_problen The fields match1 match2 and index will be used later in the code in order to map variables to the corresponding assignment and vice versa Next we need to read in the problem instance We could implement this function within the user_io callback function see the file user master c However in order to show how it can be done explicitly we will define our own function match_read_data in user_main c to fill in the user data structure and then use sym_set_user_data to pass this structure
115. ect files in the examples directory milp c This sample code is an implementation of a basic MILP solver using SYMPHONY s C callable functions with user defined input see Section 3 3 To test the code either open the solution file milp sln choose Build milp exe and then choose Execute from the Build menu or open a command terminal choose Run on the Start menu and type cmd in the dialogue box and type devenv milp sln make all debug Debug milp exe milpOsi c This sample code is an implementation of a basic MILP solver using SYM PHONY s C callable functions through OsiSym interface with user defined input see Section 3 3 2 To test the code either open the solution file milpOsi sln choose Build milpOsi exe and then Execute com mands from the Build menu or open a command terminal choose Run on the Start menu and type cmd in the dialogue box and type devenv milpOsi slb make all debug Debug milpOsi exe 2 2 Building From Source SYMPHONY can now use the COIN OR build system and the GNU autotools to automate the build process The build process should therefore be identical in all Unix like environments It is even possible to use this system to build COIN in Windows using the Microsoft Visual C compiler if you have MinGW http www mingw org installed you need the GNU make command The instructions below will lead you through the steps required to compile SYMPHONY as a generic
116. ed for difficult problem instances This involves not only the important question of how to store the data but also how to move it between modules during parallel execution A final challenge in developing a generic framework such as SYMPHONY is to deal with these issues using a problem independent approach Describing a node in the search tree consists of among other things specifying which cuts and variables are initially active in the subproblem In fact the vast majority of the methods in BCP that depend on the model are related to generating manipulating and storing the cuts and variables Hence SYMPHONY can be considered an object oriented framework with the central objects being the cuts and variables From the user s perspective implementing a BCP algorithm using SYMPHONY consists primarily of specifying various properties of objects such as how they are generated how they are represented and how they should be realized within the context of a particular subproblem With this approach we achieved the black box structure by separating these problem specific functions from the rest of the implementation The internal library interfaces with the user s subroutines through a well defined Application Program Interface API see Section 6 3 and independently performs all the normal functions of BCP tree management LP solution and cut pool management as well as inter process communication when parallelism is employed
117. ed problem Note that in order to use this feature the sensitivity_analysis parameter needs to be set before solving the original problem Arguments sym_ environment env IN Pointer to the SYMPHONY environment int cnt IN The number of the non zero elements in the new right hand side vector int new_rhs_ind IN Array of the column indices of these non zero ele ments double new_rhs_val IN Array of the values of these non zero elements double lb_for_new_rhs OUT Pointer to a double indicating the lower bound ob tained for the new problem Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 147 gt sym_get_ub_for_new_rhs int sym_get_ub_for_new_rhs sym_environment env int cnt int new_rhs_ind double new_rhs_val double ub_for_new_rhs Description This routine is used for a basic sensitivity analysis of the right hand side case It returns a quick upper bound for the problem with a modified right hand side using the information gathered from the branching tree of the original solved problem Note that in order to use this feature the sensitivity analysis parameter needs to be set before solving the original problem Arguments sym_environment env IN Pointer to the SYMPHONY environment int cnt IN The number of the non zero elements in the new right hand side vector int new_rhs_ind IN Array of the column indices of these n
118. ed unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 102 gt sym_get_num_elements int sym_get_num_elements sym_environment env int numelems Description This routine is used to get the number of non zero entries of the constraint matrix of the current problem Arguments sym_environment env IN Pointer to the SYMPHONY environment int numelems OUT Pointer to an integer indicating the number of non zero elements Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 103 gt sym_get_col_lower int sym_get_col_lower sym_environment env double collb Description This routine is used to get the lower bounds of the variables Arguments sym_environment env IN Pointer to the SYMPHONY environment double collb OUT Pointer to a double type array to be filled by the column lower bounds Note that the size of this array has to be at least the number of columns Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 104 gt sym_get_col_upper int sym_get_col_upper sym_environment env double colub Description This routine is used to get the upper bounds of the variables Arguments sym_environment env IN Pointer to the SYMPHONY environment double colub OUT Pointer to a double type array to be filled by
119. efault settings and options The corresponding value is given in curly braces for convenience 6 4 1 Global parameters verbosity integer 0 Sets the verbosity of all modules to the given value In general the greater this number the more verbose each module is Experiment to find out what this means random_seed integer 17 A random seed granularity double 1e 6 Should be set to the minimum difference between two distinct objective function values less the epsilon tolerance E g if every variable is integral and the objective coefficients are integral then for any feasible solution the objective value is integer so granularity could be correctly set to 99999 upper_bound double none The value of the best known upper bound probname string empty string The name of the problem name infile_name string empty string The name of the input file that was read by F or the TL flag 6 4 2 Master module parameters M_verbosity integer 0 M random seed integer 17 A random seed just for the Master module upper_bound double no upper bound This parameter is used if the user wants to artifi cially impose an upper bound for instance if a solution of that value is already known 220 lower_bound double no lower bound This parameter is used if the user wants to artificially impose a lower bound upper_bound_estimate double no estimate This parameter is used
120. en 1 the number of known violated cuts that can be added to the problem when this function is invoked 2 the constraints that are slack in the LP relaxation 3 the slack cuts not in the matrix that could be branched on more on this later and 4 the solution to the current LP relaxation the user must decide whether to branch or not Branching can be done either on variables or slack cuts A pool of slack cuts which has been removed from the problem and kept for possible branching is passed to the user If any of these happen to actually be violated it is up to the user to determine this they can be passed back as branching candidate type VIOLATED SLACK and will be added into the current relaxation In this case branching does not have to occur the structure of the candidates array is described below in user_select_candidates This function has two outputs The first output is action which can take four values USER__DO_BRANCH if the user wants to branch USER__DO_NOT_BRANCH if he doesn t want to branch USER__BRANCH_IF_MUST if he wants to branch only if there are no known violated cuts or finally USER__BRANCH_IF_TAILOFF if he wants to branch in case tailing off is detected The second output is the number of candidates and their description In this function the only sensible candidates are VIOLATED _SLACKs There is no post processing but in case branching is selected the col_gen before branch function is invoked before t
121. en solver calls Service requests from the user through the API for problem data problem modification and parameter modification 4 3 3 2 The Tree Management Module The tree manager controls the overall execution of the algorithm It tracks the status of its worker modules as well as that of the search tree and distributes the subproblems to be processed to the node processing module s Functions performed by the tree management module are Receive data for the root node and place it on the list of candidates for processing Receive data for subproblems to be held for later processing Handle requests from linear programming modules to release a subproblem for processing Receive branching object information set up data structures for the children and add them to the list of candidate subproblems Keep track of the global upper bound and notify all node processing modules when it changes Write current state information out to disk periodically to allow a restart in the event of a system crash Keep track of run data and send it to the master program at termination 4 3 3 3 The Node Processing Module The node processing NP module is the most complex and computationally intensive of the five processes Its job is to perform the bounding and branching operations These operations are of course central to the performance of the algorithm Functions performed by the LP module are Inform the tree manager when a new subproblem i
122. ent env IN Pointer to the SYMPHONY environment Return values TRUE The problem was abandoned FALSE The problem was not abandoned 99 6 1 4 Data Query Functions gt sym_create_copy_mip_desc MIPdesc sym_create_copy_mip_desc sym_environment env Description This routine is used to copy the problem description loaded to the environment Arguments sym_ environment env IN Pointer to the SYMPHONY environment Return values NULL An empty environment is passed in or there is no problem descrip tion loaded to the environment MIPdesc Pointer to the copy of the problem description 100 gt sym_get_num_cols int sym_get_num_cols sym_environment env int numcols Description This routine is used to get the number of the columns of the current problem Arguments sym_environment env IN Pointer to the SYMPHONY environment int numcols OUT Pointer to an integer indicating the number of columns Return values FUNCTION TERMINATED ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 101 gt sym_get_num_rows int sym_get_num_cols sym_environment env int numrows Description This routine is used to get the number of the rows of the current problem Arguments sym_environment env IN Pointer to the SYMPHONY environment int numrows OUT Pointer to an integer indicating the number of rows Return values FUNCTION_TERMINATED_ABNORMALLY Function invok
123. ent ob tain the source code as described earlier As before you then have three options You can either build using the MSVC IDE build on the command line with MSVC executable I7 or use the NMAKE utility The below instructions are for MSVC Version 6 but building in other versions should be similar All of the following commands should be executed in the SYMPHONY 5 2 3 Applications XXX MSVisualStudio v8 directory where XXX is the name of the application to be built 2 2 4 1 Building With the MSVC IDE Open the solution file xxx sln The configuration steps are exactly the same as that described in Section 2 2 3 1 The only difference is that you are using the xxx project instead of the symphony project Go through the same steps to configure Once you have the proper settings choose Build xxx exe from the Build menu This should successfully build the executable To test the executable right click on the xxx project go to the Debug tab and set the program arguments to F sample xxx Note that command line switches are Unix style Now choose Execute from the build menu and you have a working branch and bound solver 2 2 4 2 Building in a Windows Terminal Open a command terminal choose Run on the Start menu and type cmd in the dialogue box and type devenv xxx sln make all where xxx is the name of the application This will create both the debug and release versions of the application executable If you w
124. ep_warm_start 222 load_balance_iter 221 load_balance_level 221 logging 222 logging interval 223 lp_debug 220 Ip_exe 220 Ip_mach_num 220 max_active_nodes 220 max_cp_num 220 node_limit 224 node_selection_rule 221 price_in_root 223 sensitivity_analysis 224 time_limit 223 TM random seed 221 TM_verbosity 220 trim_search_tree 223 unconditional_dive_frac 221 use_cg 220 vbc_emulation 223 warm start 223 warm_start_node_level 222 warm_start_node_level_ratio 222 warm_start_node_limit 222 warm_start_node_ratio 222 245
125. er bound on the optimal value of the subproblem or 3 a message pruned indicating that the subproblem should not be considered further Step 1 Set C L Step 2 Solve the LP min cx ax lt BV a 6 C Step 3 If the LP has a feasible solution then go to Step 4 Otherwise STOP and output pruned This subproblem has no feasible solutions Step 4 If ct lt a then go to Step 5 Otherwise STOP and output pruned This subproblem cannot produce a solution of value better than a Step 5 If is the incidence vector of some 3 S then is the optimal solution to this subproblem STOP and output as s Otherwise apply separation algorithms and heuristics to to get a set of violated inequalities C If C then c is a lower bound on the value of an optimal element of S STOP and return and the lower bound cz Otherwise set C C UC and go to Step 2 Figure 4 1 Bounding in the branch and cut algorithm of algorithms is known as LP based branch and bound Typically the integrality constraints of an integer programming formulation of the problem are relaxed to obtain a LP relaxation which is then solved to obtain a lower bound for the problem In 29 Padberg and Rinaldi improved on this basic idea by describing a method of using globally valid inequalities i e inequalities valid for the convex hull of integer solutions to strengthen the LP relaxation They called this technique branch
126. er is used only if the parameter fp_enabled is set to SYM FEAS_PUMP_REPEATED 2 Otherwise the frequency is determined automatically based on some preset rules fp max cycles integer 100 Determines the maximum number of LPs that can be solved in a call to the feasibility pump heuristic A higher number might be helpful in finding a better feasible solution but may result in more time spent in the heuristic fp_time_limit double 50 If a feasible solution has been found this parameter determines the time in seconds that can be spent on the feasibility pump heuristic If a solution has not been found yet the parameter fp_max_initial_time is used fp_max_initial_time double 100 If a feasible solution has not been found this parameter determines the time in seconds that can be spent on the feasibility pump heuristic If a solution has been found the parameter fp time limit is used fp min gap double 0 5 If the relative gap between the lower and the upper bounds falls below the value of this parameter feasibility pump is not called fp flip fraction double 0 1 When the feasibility pump gets stuck in a cycle this fraction of binary variables are flipped The variables are selected randomly Increasing the value of this parameter may result in the pump getting stuck fewer number of times but the time to solve LPs after flipping may increase substantially fp_poor_sol_lim_fac integer 10 Sometimes the f
127. er module along with a corresponding function called user _receive_ data in the module These two functions work in tandem to transport the user s data from the maser where it can be read in from a file to the proper module for processing There are also a number of other tandem pairs of send and receive functions that are used to transport user data from place to place All data are sent in the form of arrays of either type char int or double or as strings To send an array the user has simply to invoke the function send_XXX_array XXX array int length where XXX is one of the previously listed types To receive that array there is a corresponding function called receive_ _array array int length When receiving an array the user must first allocate the appropriate amount of memory In cases where variable length arrays need to be passed the user must first pass the length of the array as a separate array of length one and then the array itself In the receive function this allows the length to be received first so that the proper amount of space can be allocated before receiving the array itself Note that data must be received in exactly the same order as it was passed as data is read linearly into and out of the message buffer The easiest way to ensure this is done properly is to simply copy the send statements into the receive function and change the function names It may then be necessary to add some allocation statements in b
128. es for sequential computation and yet is conceptually easy to parallelize Unfortunately this approach does have limited scalability For further discussions on the scalability of BCP algorithms and approaches to improving it see 30 and 36 4 5 1 Parallel Configurations SYMPHONY supports numerous configurations ranging from completely sequential to fully par allel allowing efficient execution in many different computational settings As described in the previous section there are five modules in the standard distributed configuration Various subsets of these modules can be combined to form separate executables capable of communicating with each other across a network When two or more modules are combined they simply communicate through shared memory instead of through message passing However they are also forced to run in sequential fashion in this case unless the user chooses to enable threading using an OpenMP compliant compiler see next section As an example the default distributed configuration includes a separate executable for each module type allowing full parallelism However if cut generation is fast and not memory intensive it may not be worthwhile to have the NP module and its associated cut generator work independently as this increases communication overhead without much potential benefit In this case the cut generator functions can be called directly from the NP module creating a single more efficient executabl
129. es that allows SYMPHONY to report the solution back in terms of column names instead of user indices If the subproblem has n variables and m constraints the constraints are given by a constraint coefficient matrix of size m x n described in the next paragraph The sense of each constraint the right hand side values and bounds on the right hand side called range are vectors are of size m The objective function coefficients and the lower and upper bounds on the variables are vectors of length n The sense of each constraint can be either L lt E G gt or R ranged For non ranged rows the range value is 0 for a ranged row the range value must be non negative and the constraint means that the row activity level has to be between the right hand side value and the right hand side increased by the range value Since the coefficient matrix is very often sparse only the nonzero entries are stored Each entry of the matrix has a column index a row index and a coefficient value associated with it A matrix is specified in the form of the three arrays matval matind and matbeg The array matval contains the values of the nonzero entries of the matrix in column order that is all the entries for the 0 column come first then the entries for the 1 column etc The row index corresponding to each entry of matval is listed in matind both of them are of length nz the number of nonzero entries in the matrix Finally matbeg co
130. ess can be abandoned problem_type integer ZERO_ONE_PROBLEM 0 The type of problem being solved Other val ues are INTEGER_PROBLEM 1 or MIXED_INTEGER_PROBLEM 2 Caution The mixed integer option is not well tested cut_pool_check frequency integer 10 The number of iterations between sending LP solu tions to the cut pool to find violated cuts It is not advisable to check the cut pool too frequently as the cut pool module can get bogged down and the LP solution generally do not change that drastically from one iteration to the next anyway not fixed storage size integer 2048 The not fixed list is a partial list of indices of vari ables not in the matrix that have not been fixed by reduced cost Keeping this list allows SYMPHONY to avoid repricing variables an expensive operation that are not in the matrix because they have already been permanently fixed When this array reaches its maximum size no more variable indices can be stored It is therefore advisable to keep the maximum size of this array as large as possible given memory limitations max_non_dual_feas_to_add_min max_non_dual_feas_to_add_max max_non_dual_feas_to_add frac integer integer double 20 200 05 These three pa rameters determine the maximum number of non dual feasible columns that can be added in any one iteration after pricing This maximum is set to the indicated fraction of the current number of active columns unless this numbers ex
131. ess whatever user defined messages are sent to the process A write to pipe to the wish process is provided so that the user can directly issue commands there Arguments void user INOUT Pointer to the user defined data structure window win INOUT The window that received the message FILE write_to IN Pipe to the wish process Return values USER_ERROR Error Message ignored USER_SUCCESS The user processed the message 216 gt user_dg_init_window void user_dg_init_window void user window win Description The user must perform whatever initialization is necessary for processing later com mands This usually includes setting up the user s data structure for receiving and storing display data Arguments void user INOUT Pointer to the user defined data structure window win INOUT Return values USER_ERROR Error Ignored USER_SUCCESS The user successfully performed initialization 217 gt user_dg_free_window void user_dg_free_window void user window win Description The user must free any data structures allocated Arguments void user INOUT Pointer to the user defined data structure window win INOUT Return values USER_ERROR Error Ignored USER_SUCCESS The user successfully freed the data structures 218 gt user_interpret_text void user_interpret_text void user int text_length char text int owner_tid Description The user can interpret text input from the window Argument
132. eters see Section 6 4 by moving into parameters submenu SYMPHONY Display parameter Please type help to see the list of available parameters SYMPHONY Display Parameter For instance in order to display the verbosity level type verbosity SYMPHONY Display Parameter verbosity The value of verbosity 3 SYMPHONY Display Parameter As in Set submenu typing help or will display only a subset of available run time parameters However you are allowed to display the value of any of the parameters given in Section 6 4 3 1 6 Sub Menu Browsing SYMPHONY s interactive optimizer also allows the user to reach the lower level menu commands from the higher level menus In other words the user has the flexibility to use submenu commands without entering the corresponding submenu As an instance all three of the following sessions have the same result SYMPHONY display parameter verbosity o SYMPHONY display Please type help to see the display options SYMPHONY Display parameter verbosity SYMPHONY display Please type help to see the display options SYMPHONY Display parameter Please type help to see the list of available parameters SYMPHONY Display Parameter verbosity This flexibility is also enabled for the Load command and the Set submenu The followings are all valid commands 26 SYMPHONY load sample mps SYMPHONY load sample mod sample dat SYMPHONY
133. eturn values USER_ERROR Error DEFAULT is used USER_SUCCESS User generated branching candidates USER_DEFAULT Regulated by the select_candidates default parame ter but set to USER__CLOSE_TO_HALF unless overridden by the user USER__CLOSE_TO_HALF Choose variables with values closest to half USER__CLOSE_TO_HALF_AND_EXPENSIVE Choose variables with values close to half and with high objective function coefficients USER__CLOSE_TO_ONE_AND_CHEAP Choose variables with values close to one and with low objective function coefficients Wrapper invoked from select_branching_object Notes See the notes at user_shall_we_branch 186 gt user_compare_candidates int user_compare_candidates void user branch_obj can1 branch_obj can2 double ub double granularity int which_is_better Description By the time this function is invoked the children of the current search tree node corresponding to each branching candidate have been pre solved i e the objval termcode iterd and feasible fields of the can1 and can2 structures are filled out Note that if the termination code for a child is LP_D_ UNBOUNDED or LP_D_OBJLIM i e the dual problem is unbounded or the objective limit is reached then the objective value of that child is set to MAXDOUBLE 2 Similarly if the termination code is one of LP_D_ITLIM iteration limit reached LP_D_INFEASIBLE dual infeasible or LP_ABANDONED because of numerical difficulties then the
134. eturn values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 117 gt sym_get_infinity double sym_get_infinity Description This routine returns the infinity value of SYMPHONY Arguments Return values DOUBLE Infinity value of SYMPHONY 118 gt sym_get_col_solution int sym_get_col_solution sym_environment env double colsol Description This routine is used to get the post solution column values Arguments sym_environment env IN Pointer to the SYMPHONY environment double colsol OUT Pointer to a double type array to be filled by the solution vector Note that the size of this array has to be at least the number of columns Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 119 gt sym_get_row_activity double sym_get_row_activity sym_environment env double rowact Description This routine is used to get the row activities which are defined as the left hand side values i e constraint matrix times the solution Arguments sym_environment env IN Pointer to the SYMPHONY environment double rowact OUT Pointer to a double type array to be filled by the row activity values Note that the size of this array has to be at least the number of rows Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FU
135. eturn values USER_ERROR Purge every single waiting row USER_SUCCESS The user marked in delete the rows to be deleted USER_DEFAULT Described above Post processing The marked rows are deleted Wrapper invoked from receive_cuts after cuts have been added 202 gt user_free_lp int user_free_lp void user Description The user has to free all the data structures within user and also free user itself The user can use the built in macro FREE that checks the existence of a pointer before freeing it Arguments void user INOUT Pointer to the user defined LP data structure Return values USER_ERROR Error SYMPHONY ignores error message USER_SUCCESS User freed everything in the user space USER_DEFAULT There is no user memory to free Wrapper invoked from 1lp_close at module shutdown 203 6 3 3 Cut generator module callbacks Due to the relative simplicity of the cut generator there are no wrapper functions implemented for CG Consequently there are no default options and no post processing gt user_receive_cg data int user_receive_cg_data void user Description This function only has to be filled out for parallel execution and only if the TM LP and CG modules are all compiled as separate modules This would not be typical If needed the user can use this function to receive problem specific data needed for computation in the CG module The same data must be received here that was sent in the
136. etween the receive function calls 5 5 2 Shared memory Architectures In the shared memory configuration it is not necessary to use message passing to move information from one module to another since memory is globally accessible In the few cases where the user 61 would ordinarily have to pass information using message passing it is easiest and most efficient to simply copy the information to the new location This copying gets done in the send function and hence the receive function is never actually called This means that the user must perform all necessary initialization etc in the send function This makes it a little confusing to write source code which will work for all configurations However the confusion should be minimized by looking at the sample applications especially the VRP solver which works in all configurations sequential distributed parallel and shared parallel 5 6 Debugging Your Application Much of this section applies to Unix operating systems However it may also be useful for Windows users 5 6 1 The First Rule SYMPHONY has many built in options to make debugging easier The most important one however is the following rule It is easier to debug the fully sequential version than the fully distributed version Debugging parallel code is not terrible but it is more difficult to understand what is going on when you have to look at the interaction of several different modules running as separate processes
137. ew obj problem using the post solution info reset sym_close environment return the allocated memory setIntParam sym set_int_param set the integer type OSI parameter setSymParam int sym_set_int_param set the integer type SYMPHONY parameter setDblParam sym_set_dbl_param set the double type OSI parameter setSymParam double sym_set_dbl_param set the double type SYMPHONY parameter setStrParam sym_set_str_param set the string type OSI parameter setSymParam string sym_set_str_param set the string type SYMPHONY parameter getIntParam sym_get_int_param get the value of the integer type OSI parameter getSymParam int amp sym_get_int_param get the value of the integer type SYMPHONY parameter getDblParam sym_get_dbl_param get the value of the double type OSI parameter getSymParam double amp sym_get_dbl_param get the value of the double type SYMPHONY parameter getStrParam sym_get_str_param get the value of the string type OSI parameter getSymParam string amp sym_get_str_param get the value of the string type SYMPHONY parameter isProvenOptimal sym_is_proven_optimal query the problem status isProvenPrimalInfeasible sym_is_proven_primal_infeasible query the problem status isPrimalObjectiveLimitReached sym_is_target_gap_achieved query the problem status isIterationLimitReached sym_is_iteration_limit_reached query the problem status isTimeLimitReached sym_is_time_limit_reac
138. f Branch Cut and Price Master store problem data request data parameters service requests for data compute initial upper bound root node send data m store best solution handle i o ATA Cut Generator 8 generate cuts violated by al GUI 3 particular LP solution Y V display solutions 3 Tree Manager input user cuts E oO 4 maintain search tree track upper bound 3 Q service requests for lt lt node data EE gt a active node data generate children and lt upper bound process subproblems add to candidate list D S select branching objects a a If check feasibility 3 2 cut list la gt send cuts to cut pool ol i 3 LP Solver Y Cut Pool _ new cuts copy cuts oe a maintain a list of effective inequalities LP solution AQ _ subtree is finished return all cuts violated by a gt particular LP solution violated cuts Figure 4 4 Schematic overview of the branch cut and price algorithm 42 Perform problem preprocessing Initialize the solution process pass problem information to the solver modules and store the results after completion of the solve call Track the status of associated processes during parallel solution calls Act as a clearing house for output during the solution process Store warm start information betwe
139. find_first_feasible TRUE sym_set_int_param env node_selection_strategy DEPTH_FIRST_SEARCH sym_solve env sym_set_int_param env find_first_feasible FALSE sym_set_int_param env node_selection_strategy BEST_FIRST_SEARCH sym_warm_solve env Figure 3 2 Implementation of a dynamic MILP solver with SYMPHONY is made at compile time through modification of the makefile or the project settings depending on the operating system To start the solution process from a warm start the sym_warm_solve command is used SYM PHONY automatically records the warm start information resulting from the last solve call and restarts from that checkpoint if a call to sym warm solve is made Alternatively external warm start information can be loaded manually Figure 3 2 illustrates the use of the re solve capability by showing the code for implementing a solver that changes from depth first search to best first search after the first feasible solution is found The user can also modify problem data in between calls to the solver Code for doing so is shown in Figure 3 3 In this example the solver is allowed to process 100 nodes and then save the warm start information Afterward the original problem is solved to optimality then is modified and re solved from the saved checkpoint Finally SYMPHONY now also has a bicriteria solve call The applications of such a solver are numerous Besides yielding the ability to closely examine
140. fter some of the parameters have been changed or problem data has been modified from a warm start If the user plans to invoke this routine the keep_warm_start parameter must be set to TRUE before the initial call to the sym_solve routine so that SYMPHONY will collect the necessary warm starting information during the solve procedure Arguments sym_environment env INOUT Pointer to the SYMPHONY environment Return values ERROR__USER TM_OPTIMAL_SOLUTION_FOUND TM_TIME_LIMIT_EXCEEDED TM_NODE_LIMIT_EXCEEDED TM_TARGET_GAP_ACHIEVED TM_FOUND_FIRST_FEASIBLE TM_ERROR__NO_BRANCHING_CANDIDATE TM_ERROR__ILLEGAL_RETURN_CODE TM_ERROR__ NUMERICAL_INSTABILITY TM_ERROR__COMM_ERROR TM_ERROR__USER FUNCTION_TERMINATED_ABNORMALLY 81 Error User error detected in one of user_send 1p data user_send_cg_data user_send_cp_data user_receive_feasible_solution user_display_solution and user_process_own_messages functions Tree Manager TM found the optimal solution and stopped TM stopped after reaching the predefined time limit TM stopped after reaching the predefined node limit TM stopped after achieving the predefined target gap TM stopped after finding the first feasible solution Error TM stopped User didn t select branching candidate in user_select_candidates callback Error TM stopped after getting a non valid return code Error TM stopped due to some numerical difficul ties Error TM stopped due to commun
141. g a 36 Branching Operation Input A subproblem S and 2 the LP solution yielding the lower bound Output Sis Sp such that S UE Si Step 1 Determine sets 1 p of inequalities such that S Ul_ 1 S ax lt BY a B E Li and UZ Si Step 2 Set S x E S ax lt P V a Li U L where L is the set of inequalities used to describe S Figure 4 2 Branching in the branch and cut algorithm Generic Branch and Cut Algorithm Input A data array specifying the problem instance Output The global optimal solution s to the problem instance Step 1 Generate a good feasible solution using heuristics Set a c 8 Step 2 Generate the first subproblem S by constructing a small set of inequalities valid for P Set A S7 Step 3 If A STOP and output 3 as the global optimum s Otherwise choose some SEA Set A A S Process S Step 4 If the result of Step 3 is a feasible solution 3 then c5 lt cs Set 35 and a c s and go to Step 3 If the subproblem was pruned go to Step 3 Otherwise go to Step 5 Step 5 Perform the branching operation Add the set of subproblems generated to A and go to Step 3 Figure 4 3 Description of the generic branch and cut algorithm set of hyperplanes which divide the current subproblem in such a way that the current solution is not feasible for the LP relaxation of any of the new subproblems For e
142. g average is used as the global quality measure The cuts in the pool are periodically sorted by this measure and only the highest quality cuts are checked each time All duplicate cuts as well as all cuts whose number of touches exceeds or whose quality falls below specified thresholds are periodically purged from the pool to keep it as small as possible 4 4 5 2 Using Multiple Pools For several reasons it may be desirable to have multiple cut pools When there are multiple cut pools each pool is initially assigned to a particular node in the search tree After being assigned to that node the pool services requests for cuts from that node and all of its descendants until such time as one of its descendants gets assigned to another cut pool After that it continues to serve all the descendants of its assigned node that are not assigned to other cut pools Initially the first cut pool is assigned to the root node All other cut pools are unassigned During execution when a new node is sent to be processed the tree manager must determine which cut pool the node should be serviced by The default is to use the same cut pool as its parent However if there is currently an idle cut pool process either it has never been assigned to any node or all the descendants of its assigned node have been processed or reassigned then that cut pool is assigned to this new node All the cuts currently in the cut pool of its parent node are copied to the new po
143. g cuts dominates that of generating variables Our methods of representation also clearly favor such problems In a future version of the software we plan to erase this bias by adding additional functionality for handling variable generation and storage This is the approach already taken by of COIN BCP 24 For more discussion of the reasons for this bias and the differences between the treatment of cuts and variables see Section 4 4 2 2 4 4 Details of the Implementation 4 4 1 The Master Module The primary functions performed by the master module were listed in Section 4 3 3 1 Here we describe the implementational details of the various solve calls 4 4 1 1 Initial Solve Calling the initial solve method solves a given MILP from scratch as described above The first action taken is to create an instance of the tree manager module that will control execution of the algorithm If the algorithm is to be executed in parallel on a distributed architecture the master module spawns a separate tree manager process that will autonomously control the solution process The tree manager in turn creates the modules for processing the nodes of the search tree generating cuts and maintaining cut pools These modules work in concert to execute the solution process When it makes sense sets of two or more modules such as a node processing module and a cut generation module may be combined to yield a single process in which the combined modules work in
144. get_matrix int sym_get_matrix sym_environment env int nz int matbeg int matind double matval Description This routine is used to get the constraint matrix in a standard column ordered format Arguments sym_environment env IN int nz OUT int matbeg OUT int matind OUT int matval OUT Return values Pointer to the SYMPHONY environment Pointer to integer indicating the non zero elements of the constraint matrix Pointer to a double type array to be filled by the starting positions of each of column Note that the size of this array has to be at least the number of columns 1 Pointer to a double type array to be filled by the row indices corresponding to each entry of matval Note that the size of this array has to be at least the number of nonzero elements of the constraint matrix Pointer to a double type array of the values of nonzero entries of the constraint matrix in column order Note that the size of this array has to be at least the number of nonzero elements of the con straint matrix FUNCTION _TERMINATED ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 111 gt sym_get_obj_coeff int sym_get_obj_coeff sym_environment env double obj Description This routine is used to get the objective vector Arguments sym_environment env IN Pointer to the SYMPHONY environment double obj OUT Pointer to a double type array to be fille
145. gging Otherwise pruned nodes should be flushed keep_warm_start boolean FALSE Turning this parameter on will have exactly the same im pact with setting the keep description of pruned to KEEP_IN_MEMORY 2 This will allow SYMPHONY to keep all the necessary information obtained from the branching tree of the original problem to be able to warm start after a parameter or problem data modification Thus if it is intended to warm start later the user should set this parameter before solving the original problem warm_start_node_limit integer SYM_INFINITY Setting this parameter will start the warm start routine using only the first warm_start_node_limit nodes generated during the previous solve procedure The rest of the tree will be trimmed 225 warm_start_node_ratio double 0 0 Setting this parameter will start the warm start routine using only the first warm_start_node_ratio of the nodes generated during the previous solve procedure warm_start_node_level integer SYM_INFINITY Setting this parameter will start the warm start routine using all the nodes above the level warm_start_node level of the tree generated during the previous solve procedure The rest of the tree will be trimmed warm start node level ratio double 0 0 Setting this parameter will start the warm start routine using all the nodes above the level warm_start_node_level of the warm start tree depth The rest of the tree will be trimmed logg
146. h Symposium on Parallel and Distributed Processing pages 392 401 Los Alamitos CA 1995 IEEE Computer Society Press R Solanki Generating the noninferior set in mixed integer biobjective linear programs an application to a location problem Computers and Operations Research 18 1 15 1991 S Tschoke and T Polzer Portable Parallel Branch and Bound Library User Manual Library Version 2 0 Department of Computer Science University of Paderborn Y Xu T K Ralphs L Lad nyi and M J Saltzman ALPS A framework for implementing parallel search algorithms In Proceedings of the Ninth INFORMS Computing Society Confer ence pages 319 334 2005 239 240 Index of C API Functions sym_add_col 135 sym_add_row 136 sym_close_environment 84 sym_create_copy_environmet 71 sym_create_copy_mip_desc 98 sym_create_copy_warm start 144 sym_create_permanent_cut_pools 81 sym_delete_cols 137 sym_delete_rows 138 sym_delete_warm_start 141 sym_explicit_load_problem 75 sym_find_initial_bounds 73 sym_get_col_lower 102 sym_get_col_solution 117 sym_get_col_upper 103 sym_get_dbl_param 90 sym_get_infinity 116 sym_get_int_param 89 sym_get_iteration_count 121 sym_get_lb_for_new_obj 147 sym_get_lb_for_new_rhs 145 sym get_matrix 109 sym_get_num_cols 99 sym_get_num_elements 101 sym_get_num_rows 100 sym_get_obj2_coeff 111 sym_get_obj_coeff 110 sym_get_obj_sense 112 sym_get_obj_val 119 sym_get_primal_bound 120 sym_get
147. han 0 means there is no limit find first_feasible boolean FALSE Whether to stop after finding the first feasible solu tion or not sensitivity_analysis boolean FALSE If the user wants to do the rudimentary sensitivity analysis which will give a lower bound for the problem modified by the right hand side then this parameter has to be set before solving the original problem If it is set SYMPHONY will keep the necessary information from the solution processes of the original problem to be able to do the sensitivity analysis later 6 4 5 LP parameters LP verbosity integer 0 Verbosity level of the LP module set_obj_upper_lim boolean FALSE Whether to stop solving the LP relaxation when it s op timal value is provably higher than the global upper bound There are some advantages to continuing the solution process anyway For instance this results in the highest possible lower bound On the other hand if the matrix is full this node will be pruned anyway and the rest of the computation is pointless This option should be set at FALSE for column generation since the LP dual values may not be reliable otherwise 227 try_to_recover_from_error boolean TRUE Indicates what should be done in case the LP solver is unable to solve a particular LP relaxation because of numerical problems It is possible to recover from this situation but further results may be suspect On the other hand the entire solution proc
148. he L switch is used to specify the data file if a file in LP format is being read in 154 gt user_initialize int user_initialize void user Description The user allocates space for and initializes the user defined data structures for the master module Arguments void user OUT Pointer to the user defined data structure Return values USER_ERROR Error SYMPHONY exits USER_SUCCESS Initialization is done USER DEFAULT There is no user defined data structure this can be the case if the default parser is being used to read in either an MPS or GMPL AMPL file 155 gt user_readparams int user_readparams void user char filename int argc char argv Description The user can optionally read in parameter settings from the file named filename specified on the command line using the f switch The parameter file filename can contain both SYMPHONY s built in parameters and user defined parameters If desired the user can open this file for reading scan the file for lines that contain user parameter settings and then read the parameter settings A shell for doing this is set up in the in the file SYMPHONY 5 0 USER user_master c Also see the file Master master_io c to see how SYMPHONY does this The user can also parse the command line arguments for user settings A shell for doing this is also set up in the file SYMPHONY 5 0 USER user_master c Upper case letters are reserved for user defined command line switches
149. he above variables The number of cuts generated The cuts and the corresponding rows Error Interpreted as if no cuts were generated Cuts were generated No cuts were generated By default SYMPHONY uses the CGL to generate generic cuts according to parameter settings Generate generic CGL cuts according to parameter settings No additional cuts are generated SYMPHONY checks if any of the newly generated cuts are already in the local pool Wrapper invoked from receive_cuts before the cuts from the CG module are re ceived Since the user will probably use this function to generate tableau dependent cuts it is highly unlikely that any of the new cuts would already be in the pool Therefore the user will probably return USER_AND_PP to force SYMPHONY to skip post processing Notes e Just like in user_unpack_cuts the user has to allocate space for the rows 199 e Unless the name field of a cut is explicitly set to CUT__SEND_TO_CP SYM PHONY will assume that the cut is locally valid only and set that field to CUT__DO_NOT_SEND_TO_CP 200 gt user_print_stat_on_cuts_added int user_print_stat_on_cuts_added void user int rownum waiting_row rows Description The user can print out some information if he wishes to on the cuts that will be added to the LP formulation The default is to print out the number of cuts added Arguments void user IN Pointer to the user defined LP data structure int rownum I
150. he branching would take place If that function finds dual infeasible variables then instead of branching they are added to the LP relaxation and the problem is resolved Note that the behavior of the col_gen_before_branch is governed by the colgen_strat TM parameters 182 Arguments void user IN Pointer to the user defined LP data struc ture double lpetol IN The e tolerance of the LP solver int cutnum IN The number of violated cuts known before invoking this function that could be added to the problem instead of branching int slacks_in_matrix_num IN Number of slack constraints in the matrix cut_data slacks_in matrix IN The description of the cuts corresponding to these constraints see Section 6 3 2 1 int slack_cut_num IN The number of slack cuts not in the matrix cut_data slack_cuts IN Array of pointers to these cuts see Section 6 3 2 1 int varnum IN The number of variables in the current lp relaxation the length of the following three arrays var_desc vars IN Description of the variables in the relax ation double x IN The corresponding solution values in the optimal solution to the relaxation char status IN The stati of the variables There are five possible status values NOT_FIXED TEMP_ FIXED_TO_UB PERM_FIXED_TO_UB TEMP_ FIXED_TO_LB and PERM_FIXED_TO_LB int cand_num OUT Pointer to the number of candidates re turned the length of candidates candidate candidates
151. he user must also implement a function to free it The functions for freeing the user data structures in each module are called user_free_ where is the module These functions are called by SYMPHONY at the time when other data structures for the modules are being freed and the module is being closed By default for sequential computation there is one common user data structure for all modules and the pointer to that data structure is passed to all user functions regardless of the module This setup should work fine for most sequential applications In parallel however pointers cannot be shared between modules and data must be explicitly passed In this case it is sometimes more efficient to maintain in each module only the data necessary to perform the functions of that module 5 5 Parallel Implementation 5 5 1 Distributed memory Architectures While the implementation of SY MP HON Y strives to shield the user from having to know anything about communications protocols or the specifics of inter process communication it may be neces sary for the user to pass information from one module to another in order to implement a parallel application For instance the user may want to pass data describing the problem instance to the LP process after reading them in from a file in the master process For the purpose of passing user data from the master process to other processes a customization function called user_send_ _data is provided in the mast
152. hed query the problem status isTargetGapReached sym_is_target_gap_achieved query the problem status getNumCols sym_get_num_cols get the number of columns getNumRows sym_get_num_rows get the number of rows getNumElements sym_get_num_elements get the number of nonzero elements getColLower sym_get_col lower get the column lower bounds getColUpper sym_get_col_upper get the column upper bounds getRowSense sym_get_row_sense get the row senses getRightHandSide sym_get_rhs get the rhs values getRowRange sym_get_row_range get the row range values getRowLower sym_get_row_lower 159 the row lower bounds getRowUpper sym_get_row_upper get the row upper bounds getObjCoefficients sym_get_obj_coeff get the objective function vector C Interface C Interface Description getObjSense sym_get _obj_sense get the objective sense isContinuous sym_is_continuous query the variable type isBinary sym_is binary query the variable type isInteger sym _is integer query the variable type isIntegerNonBinary query the variable type isFreeBinary sym_is_binary query the variable type getMatrixByRow get the constraint matrix by row oriented getMatrixByCol get the constraint matrix by column oriented getInfinity get the infinity definition of SYMPHONY getCo
153. here gamma 1 0 and tau 0 0 or from scratch Currently this option is supported if only the supported solutions are desired to be found trim_warm_tree boolean FALSE Whether to trim the warm start tree before re solving This consists of locating nodes whose descendants are all likely to be pruned in the resolve and eliminating those descendants in favor of processing the parent node itself mc compare solution tolerance double 0 001 Ifthe difference between the objective values of two solutions to be compared during the bicriteria solution procedure are less than this tolerance then assume them to be equal mc_binary_search_tolerance double 0 The tolerance to be used to differentiate the gamma values if binary search is used during the bicriteria solution procedure A value greater than zero will cause the binary search to be activated prep_level integer 5 Determines the level of preprocessing that should be done on the cur rent MILP instance A level of less than 0 means that no preprocessing will be done At level 2 basic presolve routines are used At higher levels more advanced routines are deployed At level 5 valid implications are derived 221 prep_dive level integer 5 When a variable has been modified by preprocessing then these changes can be used to improve other variables and constraints in the instance as well This parameter controls how many times can we recursively try to improve the inst
154. hing on a variable can be thought of as imposing a constraint with a single unit entry in the appropriate column Following is a list of the fields of the branch obj data structure which must be set by the user char type Can take five values CANDIDATE VARIABLE The object is a variable CANDIDATE_CUT_IN MATRIX The object is a cut it must be slack which is in the current formulation CANDIDATE CUT_NOT_IN MATRIX The object is a cut it must be slack which has been deleted from the formulation and is listed among the slack cuts VIOLATED_SLACK The object is not offered as a candidate for branching but rather it is selected because it was among the slack cuts but became violated again SLACK_TO_BE_DISCARDED The object is not selected as a candidate for branching rather it is selected because it is a slack cut which should be discarded even from the list of slack cuts int position The position of the object in the appropriate array which is one of vars slacks_in matrix or slack_cuts waiting row row Used only if the type is CANDIDATE_CUT_NOT_IN MATRIX or VIOLATED_SLACK In these cases this field holds the row extension corresponding to the cut This structure can be filled out easily using a call to user_unpack_cuts int child_num The number of children of this branching object 185 char sense double rhs double range int branch The description of the children These arrays determine the sense rhs etc for the cut to be imposed in ea
155. iary cut generator More explicitly here are the functions of the cut pool module e Receive cuts generated by other modules and store them e Receive an LP solution and return a set of cuts which this solution violates e Periodically purge ineffective and duplicate cuts to control its size 4 3 4 Algorithm Summary Currently SYMPHONY is what is known as a single pool BCP algorithm The term single pool refers to the fact that there is a single central list of candidate subproblems to be processed which is maintained by the tree manager Most sequential implementations use such a single pool scheme However other schemes may be used in parallel implementations For a description of various types of parallel branch and bound see 16 The user begins by initializing the SYMPHONY environment and can then invoke subroutines for reading in parameters and problem data finding an initial upper bound and designating the initial set of active cuts and variables in the root node Once the user invokes a solve routine a tree manager is created to manage the solution process The tree manager module in turn sets up the cut pool module s the linear programming module s and the cut generator module s Currently there are three solve calls supported by the API The first call is the initial solve see Section 4 4 1 1 which solves the problem from scratch without using warm start information The second type of solve call is a warm solve wh
156. ic implementation of the algorithm described in 31 along with a number of methods for approximating the set of Pareto outcomes To support these ca pabilities we have extended the OSI interface so that it allows the user to define a second objec tive function Of course we have also added a method for invoking this bicriteria solver called multiCriteriaBranchAndBound Relaxing the uniform dominance requirement requires the un derlying ILP solver to have the ability to generate among all optimal solutions to a ILP with a primary objective a solution minimizing a given secondary objective We added this capability to SYMPHONY through the use of optimality cuts as described in 31 Because implementing the algorithm requires the solution of a sequence of ILPs that vary only in their objective functions it is possible to use warm starting to our advantage Although the linearization of 4 4 requires modifying the constraint matrix from iteration to iteration it is easy to show that these modifications cannot invalidate the basis In the case of enumerating all supported outcomes only the objective function is modified from one iteration to the next In both cases we save warm start information from the solution of the first ILP in the sequence and use it for each subsequent computation 4 4 2 The Node Processing Module The NP module is at the core of the algorithm as it performs the processing and bounding oper ations for each subproble
157. ication error Error TM stopped User error detected in one of user callbacks called during TM processes Function invoked unsuccessfully gt sym_mc_solve int sym_mc_solve sym_environment env Description This routine is used to solve the loaded problem as a multicriteria problem For this func tion a second objective function must be set either by calling the sym_set_obj2_coeff function or by passing it directly using the sym_explict_load_problem function Arguments sym_environment env INOUT Pointer to the SYMPHONY environment Return values ERROR__USER TM_OPTIMAL_SOLUTION_FOUND TM_ERROR__NO BRANCHING_CANDIDATE TM_ERROR__ILLEGAL_RETURN_CODE TM_ERROR__NUMERICAL_INSTABILITY TM_ERROR__COMM_ERROR TM_ERROR__USER FUNCTION_TERMINATED_ABNORMALLY 82 Error User error detected in one of user_send lp data user_send_cg_data user_send_cp_data user_receive_feasible_solution user_display_solution user_process_own_messages functions The set of supported or nondominated solutions have been found Error TM stopped User didn t select branching candidate in user_select_candidates callback Error TM stopped after getting a non valid return code Error TM stopped due to some numerical difficul ties Error TM stopped due to communication error Error TM stopped User error detected in one of user callbacks activated by user and invoked during TM processes Function invoked unsu
158. ich solves the problem using previously computed warm 44 start information see Section 4 4 1 2 Finally there is a multicriteria solve call which is used to enumerate efficient solutions to a given multicriteria MILP see Section 4 4 1 3 During the solution process the tree manager functions control the execution by maintaining the list of candidate subproblems and sending them to the NP modules as they become idle The NP modules receive nodes from the tree manager process them branch if required and send back the identity of the chosen branching object to the tree manager which in turn generates the children and places them on the list of candidates to be processed see Section 4 4 2 3 for a description of the branching operation A schematic summary of the algorithm is shown in Figure 4 4 The preference ordering for processing nodes is a run time parameter Typically the node with the smallest lower bound is chosen to be processed next since this strategy minimizes the overall size of the search tree However at times it is advantageous to dive down in the tree The concepts of diving and search chains introduced in Section 4 4 3 extend the basic best first approach We mentioned earlier that cuts and variables can be treated in a somewhat symmetric fashion However it should be clear by now that our current implementation favors the implementation of branch and cut algorithms where the computational effort spent generatin
159. in Debug and or Release directories The library together with the header files in SYMPHONY includel can then be used to call SYMPHONY from any C C code The API for calling SYMPHONY is described in Section 3 3 e Test the executable by opening Windows Explorer and double clicking on symphony exe in the Debug subdirectory This should open a Window in which the interactive solver will appear Type help or to see a list of available commands or see Chapter 3 for instructions on using the interactive solver e If you modify the source code of SYMPHONY type devenv symphony sln make all rebuild in order to clean and rebuild everything 16 2 2 3 3 Building with the MSVC compiler in CYGWIN It is possible to perform at automated build of SYMPHONY using the MSVC compiler cl with GNU autotools in the CYGWIN environment To do so follow the instuctions for building in Unix like environments see Section 2 2 2 except when configuring use the command configure enable doscompile msvc 2 2 3 4 Building with the NMAKE Utility e Go to MSVisualStudio directory and edit the sym mak makefile to reflect your environment This involves specifying the LP solver to be used assigning some variables and setting various paths Only minor edits should be required An explanation of what has to be set is contained in the comments in the makefile Note that you have to first create the COIN libraries Cg1 Clp Osi OsiClp and CoinUtils e O
160. in some subproblem It is up to the user if desired to designate a compact representation for each class of constraints that is to be generated and to implement subroutines for converting from this compact representation to a matrix row given the list of active variables For instance suppose that the set of nonzero variables in a particular class of constraints corresponds to the set of edges across a cut in a graph Instead of storing the indices of each variable explicitly one could simply store the set of nodes on one side shore of the cut as a bit array The constraint could then be constructed easily for any particular set of active variables edges Just as with variables the constraints are divided into core constraints and extra constraints The core constraints are those that are active in every subproblem whereas the extra constraints can be generated dynamically and are free to enter and leave as appropriate Obviously the set of core constraints must be known and constructed explicitly by the user Extra constraints on the other hand are generated dynamically by the cut generator as they are violated As with variables a good set of core constraints can have a significant effect on efficiency Note that the user is not required to designate a compact representation scheme Constraints can simply be represented explicitly as matrix rows with respect to the global set of variables However designating a compact form can resu
161. ine int sym_parse_command_line sym_environment env int argc char argv Description This routine parses the command line arguments It must be called whenever the user specifies any of SYMPHONY s built in command line switches For instance this is the case when the user specifies the location of an MPS LP or GMPL file using the F or L switch or when the user specifies the location of a parameter file with the f switch This command also invokes the user callback function user_readparams see Section 6 3 1 Arguments sym_ environment env INOUT Pointer to the SYMPHONY environment int argc IN The number of command line arguments char argv IN Array of pointers to these arguments Return values ERROR__USER Error User error detected in user_readparams function FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 74 gt sym_find_initial_bounds int sym_find_initial_bounds sym_environment env Description This routine invokes the user callback user_start_heurs see Section 6 3 1 to set the priori bound for the problem Arguments sym_ environment env INOUT Pointer to the SYMPHONY environment Return values ERROR__USER Error User error detected in user_start_heurs function FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 79 gt sym_load_problem in
162. ing integer NO_LOGGING O Whether or not to write out the state of the search tree and all other necessary data to disk periodically in order to allow a warm start in the case of a system crash or to allow periodic viewing with VBCTOOL If the value of this parameter is set to FULL_LOGGING 1 then all information needed to warm start the calculation will written out periodically The next two lines of the parameter file following should contain the keywords tree_log file name and cut_log file name along with corresponding file names as values These will be the files used to record the search tree and related data and the list of cuts needed to reconstruct the tree If the value of the parameter is set to VBC_TOOL 2 then only the information VBCTOOL needs to display the tree will be logged This is not really a very useful option since a live picture of the tree can be obtained using the vbc_emulation parameter described below logging interval integer 1800 Interval in seconds between writing out the above log files warm_start boolean 0 Used to allow the tree manager to make a warm start by reading in previously written log files If this option is set then the two line following must start with the keywords warm_start_tree_file_name and warm_start_cut_file_name and include the appropriate file names as the corresponding values vbc_emulation integer NO_VBC_EMULATION 0 Determines whether or not to employ th
163. ing search chains but also should decrease the time needed to process each search tree node by allowing variables to be fixed by reduced cost 4 4 3 3 The Two Phase Algorithm If no heuristic subroutine is available for generating feasible solutions quickly then a unique two phase algorithm can also be invoked In the two phase method the algorithm is first run to completion on a specified set of core variables Any node that would have been pruned in the first phase is instead sent to a pool of candidates for the second phase If the set of core variables is small but well chosen this first phase should be finished quickly and should result in a near optimal 52 solution In addition the first phase will produce a list of useful cuts Using the upper bound and the list of cuts from the first phase the root node is repriced that is 1t is reprocessed with the full set of variables and cuts The hope is that most or all of the variables not included in the first phase will be priced out of the problem in the new root node Any variable thus priced out can be eliminated from the problem globally If we are successful at pricing out all of the inactive variables we have shown that the solution from the first phase was in fact optimal If not we must go back and price out the reduced set of extra variables in each leaf of the search tree produced during the first phase We then continue processing any node in which we fail to price out all th
164. int the reader to the survey of parallel branch and bound algorithms by Gendron and Crainic 16 as well as other references such as 10 18 32 22 54 In parallel BCP as in general branch and bound there are two major sources of parallelism First it is clear that any number of subproblems on the current candidate list can be processed simultaneously Once a subproblem has been added to the list it can be properly processed before during or after the processing of any other subproblem This is not to say that processing a particular node at a different point in the algorithm won t produce different results it most certainly will but the algorithm will terminate correctly in any case The second major source of parallelism is to parallelize the processing of individual subproblems By allowing separation to be performed in parallel with the solution of the linear programs we can theoretically process a node in little more than the amount of time it takes to solve the sequence of LP relaxations Both of these sources of parallelism can be easily exploited using the SYMPHONY framework The most straightforward parallel implementation which is the one we currently employ is a master slave model in which there is a central manager responsible for partitioning the work and parceling it out to the various slave processes that perform the actual computation The reason we chose this approach is because it allows memory efficient data structur
165. integer indicating the number of columns to be deleted int indices IN Pointer to an integer type array indicating the indices of the columns to be deleted and having a size of at least num Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully or one of the indices is out of the range of 0 number of variables 1 139 gt sym_delete_rows int sym_delete_rows sym_environment env int num int indices Description This routine is used to delete rows from the original constraint matrix Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int num IN An integer indicating the number of rows to be deleted int indices IN An array indicating the indices of the rows to be deleted and having a size of at least num Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully or one of the indices is out of the range of 0 number of variables 1 140 6 1 6 Warm Starting Functions gt sym_write_warm_start_desc int sym_write_warm_start_desc warm_start_desc ws char file Description This routine is used to write the given warm start structure to a file Arguments warm_start_desc ws IN Pointer to the warm start description to be written char file IN The name of the file the warm start is desired to be written to Return values FUN
166. into appropriate locations in the master function user send 1p data see Section 6 3 1 The two cases can be handled by means of ifdef statements See comments in the source code stubs for more details Here the user must receive all problem specific information sent from the mas ter set up necessary data structures etc Note that the data must be received in exactly the same order as it was sent in user_send_1p_data see Section 6 3 1 See Section 5 5 1 for more notes on receiving data Arguments void user OUT Pointer to the user defined LP data structure Return values USER_ERROR Error SYMPHONY aborts this LP module USER_SUCCESS User received the data successfully USER_DEFAULT User did not send any data Wrapper invoked from lp_initialize at process start 175 gt user_create_subproblem int user_create_subproblem void user int indices MIPdesc mip int maxn int maxm int maxnz Description Based on the instance data contained in the user data structure and the list of base and extra variables that are active in the current subproblem the user has to create the subproblem for the search node The matrix describing the subproblem must contain those variables whose user indices are listed in the array indices in the same order and the base constraints The extra dynamically generated constraints are added automatically by SYMPHONY after the initial subproblem description is created In this functi
167. ion This routine is used to set the objective sense By default SYMPHONY will solve a minimization problem Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int sense IN New sense of the objective function It can be 1 and 1 for minimization and maximization Otherwise SYMPHONY will assume the objective sense to be minimization Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 131 gt sym_set_col solution int sym_set_col_solution sym_environment env double colsol Description This routine is used to set the current solution if a known one exists Note that setting the column solution will not affect or help the treemanager s processes other than setting the best feasible solution and the corresponding upper bound Arguments sym_ environment env INOUT Pointer to the SYMPHONY environment double colsol IN Pointer to a double type array of the known column values Note that if the given solution is not feasi ble or if a better solution was found loaded before SYMPHONY will refuse to set the column solution and will leave this function without success Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 132 gt sym_set_primal_ bound int sym_set_primal_bound sym_environment env double bound Description
168. ional_diving ratio fractional_diving num integer 0 02 0 Diving occurs auto matically if the number of fractional variables in the child to be dived into is less than fractional_diving num or the fraction of total variables that are fractional is less than fractional_diving_ratio This overrides the other diving rules Note that in order for this 224 option to work the code must be compiled with FRACTIONAL_BRANCHING defined This is the default See the makefile for more details node selection rule integer LOWEST_LP_FIRST 0 The rule for selecting the next search tree node to be processed This rule selects the one with lowest lower bound Other possible values are HIGHEST_LP_FIRST 1 BREADTH_FIRST_SEARCH 2 and DEPTH_FIRST_SEARCH 3 load balance level integer 1 A naive attempt at load balancing on problems where sig nificant time is spent in the root node contributing to a lack of parallel speed up Only a prescribed number of iterations load_balance_iter are performed in the root node and in each subsequent node on a level less than or equal to load balance level before branching is forced in order to provide additional subproblems for the idle processors to work on This doesn t work well in general load_balance_iter integer 1 Works in tandem with the load balance level to attempt some simple load balancing See the above description keep_description_of_pruned integer DISCARD O Whether to
169. is routine is used to load a problem description into SYMPHONY manually The constraint matrix is passed in a standard column ordered format The arguments here are the same as the fields in the MIPdesc data structure discussed in Section 6 3 2 1 Please see the discussion there for a more detailed description of the arguments here Arguments sym_environment env INOUT Pointer to the SYMPHONY environment int numcols IN Number of the columns int numrows IN Number of the rows int start IN Array of the starting positions of each of column int index IN Array of the row indices corresponding to each entry of value int value IN Array of the values of nonzero entries of the con straint matrix in column order double collb IN Array of the lower bounds of the columns double colub IN Array of the upper bounds of the columns double obj IN Array of the objective function coefficients double obj2 IN Array of the second objective function coefficients when multi criteria solver is to be used char rowsen IN Array of the senses of the constraints L lt constraint E constraint G gt constraint R ranged constraint N free constraint double rowrhs IN Array of the right hand side values double rowrng IN Array of the row ranges sym_get_row_upper sym_get_row_lower if the row sense is R O otherwise char make_copy IN SYMPHONY will create the copies of these arrays for internal us
170. ive function values instead of the change in the gap ineff _cnt_to delete integer 0 Determines after how many iterations of being deemed in effective a constraint is removed from the current relaxation eff_cnt_before_cutpool integer 3 Determines after how many iterations of being deemed effective each cut will be sent to the global pool ineffective constraints integer BASIC _SLACKS_ARE_INEFFECTIVE 2 Determines under what condition a constraint is deemed ineffective in the current relaxation Other possible values are NO_CONSTRAINT_IS_INEFFECTIVE 0 NONZERO_SLACKS_ARE_INEFFECTIVE 1 and ZERO_DUAL_VALUES_ARE_INEFFECTIVE 3 base_constraints_always_effective boolean TRUE Determines whether the base con straints can ever be removed from the relaxation In some case removing the base constraints from the problem can be disastrous depending on the assumptions made by the cut generator branch_on_cuts boolean FALSE This informs the framework whether the user plans on branching on cuts or not If so there is additional bookkeeping to be done such as main taining a pool of slack cuts to be used for branching Therefore the user should not set this flag unless he actually plans on using this feature discard_slack_cuts integer DISCARD_SLACKS_BEFORE_NEW_ITERATION O Determines when the pool of slack cuts is discarded The other option is DISCARD_SLACKS_WHEN_STARTING _NEW_NODE 1 first_1p firstcu
171. ived through message passing See user_receive_cp_data in Section 6 3 4 for more discus sion Otherwise it can be copied through shared memory The easiest solution which is set up by default is to simply copy over a pointer to a single user data structure where instance data is stored The code for the two cases is put in the same source file by use of ifdef statements See the comments in the code stub for this function for more details Note that there is support for cuts generated and stored as explicit matrix rows The cut pool module is already configured to deal with such cuts so no user implemen tation is required Only the use of user defined cuts requires customization of the Cut Pool module Arguments void user IN Pointer to the user defined data structure void xuser_cp OUT Pointer to the user defined data structure for the cut pool module Return values USER_ERROR Error SYMPHONY stops USER_SUCCESS Packing is done USER DEFAULT No data to send to the cut pool no user defined cut classes or cut pool not used 165 gt user_display_solution int user_display_solution void user double lpetol int varnum int indices Description double values double objval This function is invoked when a best solution found so far is to be displayed after heuristics after the end of the first phase or the end of the whole algorithm This can be done using either a text based format or using the drawgraph module B
172. k function and is defined in a file called _wrapper c The wrapper function first collects the necessary data and hands it to the user by calling the user function Based on the return value from the user the wrapper then performs any necessary post processing All callback functions have default options so that SYMPHONY now acts as a generic MILP solver out of the box 59 In Section 6 3 the callback functions are described in detail The name of every callback function starts with user_ There are three kinds of arguments IN An argument containing information that the user might need to perform the function OUT A pointer to an argument in which the user should return a result requested data decision etc of the function INOUT An argument which contains information the user might need but also for which the user can change the value The return values for most function are as follows Return values USER_ERROR Error in the user function Printing an error message is the user s responsibility Depending on the work the user function was sup posed to do the error might be ignored and some default option used or the process aborts USER_SUCCESS The user function was implemented and executed correctly USER_DEFAULT This option means that the user function was not implemented and that SYMPHONY should either execute a default subroutine the default is one of the built in options S YMPHONY decides which one to use base
173. keep the description of pruned search tree nodes or not The reasons to do this are 1 if the user wants to write out a proof of optimality using the logging function 2 for debugging or 3 to get a visual picture of the tree using the software VBCTOOL Otherwise keeping the pruned nodes around just takes up memory There are three options if it is desired to keep some description of the pruned nodes around First their full description can be written out to disk and freed from memory KEEP_ON_DISK_FULL 1 There is not really too much you can do with this kind of file but theoretically it contains a full record of the solution process and could be used to provide a certificate of optimality if we were using exact arithmetic using an independent verifier In this case the line following keep description of pruned should be a line containing the keyword pruned_node_file_name with its corresponding value being the name of a file to which a description of the pruned nodes can be written The file does not need to exist and will be over written if it does exist If you have the software VBCTOOL then you can alternatively just write out the information VBCTOOL needs to display the tree KEEP_ON_DISK_VBC_TOOL 2 Finally the user can set the value to of this parameter to KEEP_IN_MEMORY 2 in which case all pruned nodes will be kept in memory and written out to the regular log file if that option is chosen This is really only useful for debu
174. l lead you through the steps required to compile SYMPHONY as a generic MILP solver Note that the Windows version has some limitations Detailed timing information is not currently provided Support is only provided for running in sequential mode at this time First obtain the source code by downloading from https www coin or org download source SYMPHONY Unpack the archive to create the directory SYMPHONY 5 2 3 You now have three options You can either build using the MSVC IDE build on the command line with MSVC compiler or use the NMAKE utility 2 2 3 1 Building with the MSVC Graphical Interface These instructions are for MSVC Version 8 Instructions for other versions should be similar e Go to MSVisualStudiolv8 directory and open the solution file symphony sIn e Note that there are a number of additional preprocessor definitions that control the func tionality of SYMPHONY These definitions are described in sym mak a Unix style makefile included in the distribution To enable the functionality associated with a particular defini tion simply add it to the list of definitions of 1ibSymphony project together with the required libraries and paths For instance if you want to enable GMPL reader option you need to add the directory of the header files of GLPK to the include files path add USE_GLPMPL to the defines add the GLPK library to the solution 15 e Make sure that the project symphony is set as the
175. lSolution sym_get col solution get the current best column solution getRowActivity sym get_row activity get the current row activity getObj Value sym_get obj val get the current best objective value getPrimalBound sym get primal bound get the primal upper bound getlterationCount Jteration_count far sym_g get the number of the analyzed tree nodes setObjCoeff sym_set_obj_coeff set the objective function vector setObj2Coeff sym_set_obj2 coeff set the second objective function vector setColLower sym_set_col lower set the column lower bounds setColUpper sym_set_col_upper set the column upper bounds setRowLower sym_set_row lower set the row lower bounds setRowUpper sym_set_row_upper set the row upper bounds setRowType sym_set_row type set the row characteristics setObjSense sym_set_obj_sense set the objective sense setColSolution sym_setcol solution set the current solution setContinuous sym_set_continuous set the variable type setInteger sym _set_integer set the variable type setColName sym_set_col names set the column names addCol sym_add_col add columns to the constraint matrix addRow sym_add_row add rows to the constraint matrix deleteCols sym_delete_cols delete some columns from the constraint matrix deleteRows sym_delete rows delete some rows from the const
176. lean 0 Determines if statistics on how many of each type of changes were made in the preprocessing stage are displayed 1 or not 0 keep_row ordered integer 1 When the value of this parameter is 1 a row ordered matrix is also retained for use after the preprocessing stage This capability is not yet implemented and this parameter does not have any effect prep_do_sr boolean 0 When the value of this parameter is 1 additional preprocessing is performed by solving an LP with one constraint This procedure is not thoroughly tested max sr_cnt integer 5 This parameter controls the number of single constraint LPs that are solved for each constraint in the preprocessing stage This procedure is not thoroughly tested max_aggr_row_cnt integer 0 This parameter is not used and has no effect prep_iter_limit integer 10 Determines the maximum number of times preprocessing can be done on an instance If an instance has been modified by preprocessing then the new problem can be preprocessed again to get an even better formulation This parameter puts a limit on the number of times such preprocessing can be done 222 write_mps boolean 0 Determines if an MPS file be written after all preprocessing has been performed This can be used for debugging or if the user wants to save the preprocessed instance write 1p boolean 0 Determines if an LP file be written after all preprocessing has been performed This
177. lt in large reductions in memory use if the number of variables in the problem is large 40 4 3 2 3 Search Tree Having described the basics of how objects are represented we now describe the representation of search tree nodes Since the base constraints and variables are present in every subproblem only the indices of the extra constraints and variables are stored in each node s description A complete description of the current basis is maintained to allow a warm start to the computation in each search node This basis is either inherited from the parent computed during strong branching see Section 4 4 2 3 or comes from earlier partial processing of the node itself see Section 4 4 3 3 Along with the set of active objects we must also store the identity of the object s which were branched upon to generate the node The branching operation is described in Section 4 4 2 3 Because the set of active objects and the status of the basis do not tend to change much from parent to child all of these data are stored as differences with respect to the parent when that description is smaller than the explicit one This method of storing the entire tree is highly memory efficient The list of nodes that are candidates for processing is stored in a heap ordered by a comparison function defined by the search strategy see 4 4 3 This allows efficient generation of the next node to be processed 4 3 3 Modular Implementation SY MP HON Ys functions
178. lticriteria problem This method is described in more detail in Section 4 4 1 3 sym_close_environment Frees all problem data and deletes the environment As an example of the use of the library functions Figure 3 1 shows the code for implementing a generic MILP solver with default parameter settings To read in an MPS file called sample mps and solve it using this program the following command would be issued symphony F sample mps To read and solve a model in LP format the command would be symphony L sample lp The user does not have to invoke a command to read the input file During the call to sym_parse_ command line SYMPHONY determines that the user wants to read in an MPS file During the subsequent call to sym_load_problem the file is read and the problem data stored To read an GMPL file the user would issue the command symphony F sample mod D sample dat Although the same command line switch is used to specify the model file the additional presence of the D option indicates to SYMPHONY that the model file is in GMPL format and GLPK s GMPL parser is invoked 25 Note that the interface and the code of Figure 3 1 is the same for both sequential and parallel computations The choice between sequential and parallel execution modes 29 int main int argc char argv sym_environment env sym_open_environment sym_parse_command_line env argc argv sym_load_problem env sym_set_int_param env
179. m A schematic diagram of the node processing loop is presented in Fig 4 5 The details of the implementation are discussed in the following sections 48 Solve current LP relaxation lt New variables generated ____ Y Test for fathoming Fathom Y 1 Generate variables Test for feasibility Y Restore feasibility Send solution to cut generator pool Y Y Fathom Fix variables Y Remove ineffective cuts Branch 1 1 Send effective cuts to global pool Compare branching candidates ry Receive cuts from generator pool Select branching candidates Add cuts from local pool to LP Branch Figure 4 5 Overview of the node processing loop 49 4 4 2 1 The LP Engine SYMPHONY requires the use of a third party callable library referred to as the LP engine or LP library to solve the LP relaxations once they are formulated As with the user functions SYMPHONY communicates with the LP engine through an API that converts SYMPHONY s internal data structures into those of the LP engine Currently the framework will only work with advanced simplex based LP engines such as CPLEX 9 since the LP engine must be able to accept an advanced basis and provide a variety of data to the framework during the solution process The internal data structures used for m
180. m_environment env IN Pointer to the SYMPHONY environment double rowrng OUT Pointer to a double type array to be filled by the row range values Note that the size of this array has to be at least the number of rows Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 108 gt sym_get_row_lower int sym_get_row_lower sym_environment env double rowlb Description This routine is used to get the lower bounds of the rows Arguments sym_environment env IN Pointer to the SYMPHONY environment double rowlb OUT Pointer to a double type array to be filled by the row lower bounds Note that the size of this array has to be at least the number of rows Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 109 gt sym_get_row_upper int sym_get_row_upper sym_environment env double rowub Description This routine is used to get the upper bounds of the rows Arguments sym_environment env IN Pointer to the SYMPHONY environment double rowub OUT Pointer to a double type array to be filled by the row upper bounds Note that the size of this array has to be at least the number of rows Return values FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 110 gt sym_
181. main menu displayed at the beginning of a new session AA FK FK K K K K K Rda FK FK FK K K K K FK FK FK FK FK FK FK FK K dd FK FK FK FK FK FK K K K K K 2K ak ak 2 K This is SYMPHONY Version 5 2 3 Copyright 2000 2010 Ted Ralphs All Rights Reserved Distributed under the Common Public License 1 0 add dd rrr rrr rrr rrr dae ld ae k k ae al de old k k k e lod kkk xk WELCOME TO SYMPHONY INTERACTIVE MIP SOLVER Please type help to see the main commands SYMPHONY When you type help or a list of main commands is displayed SYMPHONY help List of main commands load read a problem in mps or ampl format solve solve the problem lpsolve solve the lp relaxation of the problem set set a parameter display display optimization results and stats reset restart the optimizer help show the available commands params options quit exit leave the optimizer SYMPHONY Following is an illustration of a session to read in a sample instance SYMPHONY load Name of the file sample mps Coin00011 At line 1 NAME SAMPLE Coin00011 At line 2 ROWS Coin00011 At line 6 COLUMNS Coin00011 At line 25 RHS Coin00011 At line 28 BOUNDS Coin00011 At line 34 ENDATA 22 Coin00021 Problem SAMPLE has 2 rows 6 columns and 10 elements SYMPHONY The format of the input file is recognized from the file extension If there is none you will be prompted to define the input format SYMPHONY load Name of the
182. mained challenging to solve in practice The last two decades have seen tremendous progress in our ability to solve large scale discrete optimization problems These advances have culminated in the approach that we now call branch and cut a technique see 19 29 20 which brings the computational tools of branch and bound algorithms together with the theoretical tools of polyhedral combinatorics Indeed in 1998 Applegate Bixby Chvatal and Cook used this technique to solve a Traveling Salesman Problem instance with 13 509 cities a full order of magnitude larger than what had been possible just a decade earlier 2 and two orders of magnitude larger than the largest problem that had been solved up until 1978 This feat becomes even more impressive when one realizes that the number of variables in the standard formulation for this problem is approximately the square of the number of cities Hence we are talking about solving a problem with roughly 100 million variables There are several reasons for this impressive progress Perhaps the most important is the dramatic increase in available computing power over the last decade both in terms of processor speed and memory This increase in the power of hardware has subsequently facilitated the development of increasingly sophisticated software for optimization built on a wealth of theoretical results As software development has become a central theme of optimization research efforts many theoretical
183. matches If the other parameters are required to be modified the user can always set them directly by their C inter face names using the overlapping functions setSymParam string int setSymParam string double and setSymParam string string For instance the verbosity parameter can be set let s say to 2 either by setSymParam OsiSymVerbosity 2 or by setSymParam verbosity 2 Note that this flexibility is also supported for parameter querying functions 235 236 Bibliography E 11 12 D Applegate R Bixby V Chv tal and W Cook CONCORDE TSP solver http www tsp gatech edu concorde html D Applegate R Bixby V Chv tal and W Cook On the solution of traveling salesman problems Documenta Mathematica Extra Volume Proceedings ICM III 1998 645 656 1998 E Balas S Ceria and G Cornu jols Mixed 0 1 programming by lift and project in a branch and cut framework Management Science 42 1229 1246 1996 M Benchouche V D Cung S Dowaji B Le Cun T Mautor and C Roucairol Building a parallel branch and bound library In in Solving Combinatorial Optimization Problems in Parallel Lecture Notes in Computer Science 1054 page 201 Springer Berlin 1996 V J Bowman On the relationship of the Tchebycheff norm and the efficient frontier of multiple criteria objectives In H Thieriez editor Multiple Criteria Decision Making pages 248 258 Springer Berlin 1976 Q Chen M
184. n to be infeasible FALSE The problem was not proven to be infeasible 96 gt sym_is_iteration_limit_reached int sym_is_iteration_limit_reached sym_environment env Description This post solution query routine is used to learn whether the iteration node limit was reached It can also be used if find_first_feasible parameter was set to true before solving the problem Arguments sym_environment env IN Pointer to the SYMPHONY environment Return values TRUE The iteration limit is reached FALSE The iteration limit is not reached 97 gt sym_is_time_limit_reached int sym_is_time_limit_reached sym_environment env Description This post solution query routine is used to learn whether the time limit was reached Arguments sym_environment env IN Pointer to the SYMPHONY environment Return values TRUE Time limit was reached FALSE Time limit was not reached 98 gt sym_is_target_gap_achieved int sym_is_target_gap_achieved sym_environment env Description This post solution query routine is used to learn whether the target gap was reached Arguments sym_environment env IN Pointer to the SYMPHONY environment Return values TRUE Target gap was reached FALSE Target gap was not reached gt sym_is_abandoned int sym_is_abandoned sym_environment env Description This post solution query routine is used to learn whether the problem was abandoned for some reason Arguments sym_environm
185. nce configuration is done open a command line terminal and type nmake sym mak This will make the SYMPHONY library 1ibSymphony lib and the executable symphony in Debug directory The library together with the header files in SYMPHONY include can then be used to call SYMPHONY from any C C code The API for calling SYMPHONY is described in Section 3 3 e Test the executable by opening Windows Explorer and double clicking on symphony exe in the Debug subdirectory This should open a Window in which the interactive solver will appear Type help or to see a list of available commands or see Chapter 3 for instructions on using the interactive solver 2 2 4 Building SYMPHONY Applications As mentioned above there are a number of sample applications available as examples of how to do development with SYMPHONY These include solvers for the matching problem the set partitioning problem simple and advanced versions the vehicle routing and traveling salesman problems the mixed postman problem multi criteria knapsack problem and capacitated network routing problem These applications are contained in the SYMPHONY Applications subdirectory in the distribution There is also a white paper that guides the user through the development of the MATCH solver in the SYMPHONY Doc directory For instructions on developing your own application with SYMPHONY see Chapter 5 In order to compile SYMPHONY s applications in the Microsoft Visual C environm
186. nce configuration is done type nmake f user mak The executable user exe will be created under the Debug directory e To test the executable type Debug user exe F sample user 5 2 2 3 Using the MSVC IDE e Open the solution file user sln e The configuration steps are exactly the same with the MSVC section of SYMPHONY The only difference is that you have the user project instead of the symphony project Go through the related steps of section 2 2 3 to see how to get USER executable e Once you have the proper settings choose Build user exe from the Build menu This should successfully build the executable e To test the executable right click on the user project go to the Debug tab and set the program arguments to F sample mps Note that command line switches are Unix style e Now choose Execute from the build menu and you have a working branch and bound solver After successful compilation you can fill in the user callback functions as describe in Section 5 5 3 Writing the Callbacks For each module all callback functions are invoked from so called wrapper functions that provide the interface and also performs a default action if the user chooses not to override it Although SYMPHONY is written in C the wrapper functions provide a C style interface in which the user can either accept the default action or override it Each wrapper function is named _u where is the name of the corresponding callbac
187. nd the LP solution to the cut generator and cut pool This parameter is provided so that the user can change the default behavior without recompiling The other option is SEND_FRACTIONS 1 display solution default integer DISP_NOTHING O Determines how to display the cur rent LP solution if desired See the description of user_display_solution for other possible values This parameter is provided so that the user can change the default behavior without recompiling shall_we_branch_default integer USER_BRANCH_IF_MUST 2 Determines the de fault branching behavior Other values are USER DO NOT BRANCH O not recom mended as a default USER_DO BRANCH 1 also not recommended as a default and USER__BRANCH_IF_TAILOFF 3 This parameter is provided so that the user can change the default behavior without recompiling select_candidates_default integer USER__CLOSE_TO_HALF_AND_EXPENSIVEX10 Determines the default rule for selecting strong branching candidates Other values 231 are USER__CLOSE_TO_HALF 10 and USER__CLOSE_TO_ONE_AND_CHEAP 12 This parameter is provided so that the user can change the default behavior without recompiling compare candidates default integer HIGHEST _LOW_OBJ 2 Determines the default rule for comparing candidates See the description of user_compare_candidates for other values This parameter is provided so that the user can change the default behavior without recom piling selec
188. nded SYMPHONY s capabilities in some areas 1 4 Related Work The 1990 s witnessed a broad development of software for discrete optimization Almost without exception these new software packages were based on the techniques of branch cut and price The packages fell into two main categories those based on general purpose algorithms for solving mixed integer linear programs MILPs without the use of special structure and those facilitating the use of special structure by interfacing with user supplied problem specific subroutines We will call packages in this second category frameworks There have also been numerous special purpose codes developed for use in particular problem settings Of the two categories MILP solvers are the most common Among the dozens of offerings in this category are MINTO 27 MIPO 3 bc opt 8 and SIP 26 Generic frameworks on the other hand are far less numerous The three frameworks we have already mentioned SYMPHONY ABACUS and COIN BCP are the most full featured packages available Several others such as MINTO originated as MILP solvers but have the capability of utilizing problem specific sub routines CONCORDE 2 1 a package for solving the Traveling Salesman Problem TSP also deserves mention as the most sophisticated special purpose code developed to date Other related software includes several frameworks for implementing parallel branch and bound Frameworks for general parallel bran
189. ned data structure should be NULL on exit from this function Return values USER_ERROR Ignored USER_SUCCESS The user freed all data structures USER_DEFAULT The user has no memory to free Invoked from cg_close at module shutdown 209 6 3 4 Cut pool module callbacks Due to the relative simplicity of the cut pool there are no wrapper functions implemented for CP Consequently there are no default options and no post processing gt user_receive_cp data int user_receive_cp_data void user Description The user has to receive here all problem specific information sent from user send cp data see Section 6 3 1 function in the master module The user has to allocate space for all the data structures including user itself Note that this function is only called if the either the Tree Manager LP or CP are running as a separate process i e either COMPILE_IN_TM COMPILE_IN_LP or COMPILE_IN_CP are set to FALSE in the make file Otherwise this is done in user_send_cp_data See the description of that function for more details Arguments void user INOUT Pointer to the user defined data structure Return values USER_ERROR Error Cut pool module exits USER_SUCCESS The user received data successfully USER_DEFAULT The user did not send any data to be received Invoked from cp_initialize at module start up 210 gt user_receive_lp_solution_cp void user_receive_lp_solution_cp void user Description
190. nment env char colname Description This routine is used to set the column names Arguments sym_environment env INOUT Pointer to the SYMPHONY environment char colname IN Pointer to a string array including the column names Note that the size of this array has to be at least the number of columns Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 136 gt sym_add_col int sym_add_col sym_environment env int numelems int indices double elements double collb double colub double obj char name Description This routine is used to add a new column to the original problem description sym_environment env INOUT Pointer to the SYMPHONY environment Arguments int numelems IN int indices IN double elements IN double collb IN double colub IN double obj IN char name IN Return values FUNCTION_TERMINATED_NORMALLY An integer indicating the non zero elements of the column Pointer to an integer type array indicating the row indices of the non zero elements of the column and having a size of at least numelems Pinter to a double type array indicating the values of the non zero elements of the column and having a size of at least numelens A double indicating the lower bound of the column A double indicating the upper bound of the column A double indicating the objective coefficient value of the column
191. ntains the starting positions of each of the columns in matval and matind Thus matbegli is the position of the first entry of column i in both matval and matind By convention matbeg is allocated to be of length n 1 with matbeg n containing the position after the very last entry in matval and matind so it is very conveniently equal to nz This representation of a matrix is known as a column ordered or column major representation Below are listed the fields that must be filled out to describe a subproblem 170 int n The number of columns int m The number of rows int nz The number of nonzeros double obj_offset Constant to be added to the objective function value when printed char obj_sense Objective sense set to MAXIMIZE or MINIMIZE char is_int Indicates which variables are required to be integer int matbeg The array containing the starting positions for each column int matind The array containing the indices for each column double matval The array containing the matrix values for each column double obj The objective function coefficients for the second objective for multicriteria solve double obj2 The objective function coefficients double rhs The right hand side values for the constraints double rngval The range values for the constraints optional char sense The senses of the constraints double lb The lower bounds of the variables doubl
192. objective value of that child is set to that of the parent s Based on this information the user must choose which candidate he considers better and whether to branch on this better one immediately without checking the remaining candidates As such there are four possible answers FIRST_CANDIDATE_BETTER SECOND_CANDIDATE_BETTER FIRST_CANDIDATE_BETTER_AND_BRANCH_ON_IT and SECOND CANDIDATE BETTER AND BRANCH ON_IT An answer ending with _AND_BRANCH_ON_IT indicates that the user wants to terminate the strong branch ing process and select that particular candidate for branching There are several default options In each of them objective values of the pre solved LP relaxations are compared Arguments void user IN Pointer to the user defined LP data structure branch_obj canl IN One of the candidates to be compared branch_obj can2 IN The other candidate to be compared double ub IN The current best upper bound double granularity IN Defined tolerance int which is better OUT The user s choice See the description above 187 Return values USER_ERROR USER_SUCCESS USER_DEFAULT BIGGEST _DIFFERENCE_OBJ LOWEST_LOW_OBJ HIGHEST _LOW_OBJ LOWEST_HIGH_OBJ HIGHEST_HIGH_OBJ LOWEST_LOW_FRAC HIGHEST_LOW_FRAC LOWEST _HIGH_FRAC HIGHEST_HIGH_FRAC dren have been pre solved Error DEFAULT is used User filled out which_is_better Regulated by the compare_candidates_default parameter initially set to LOWEST_LOW_OB
193. ol to initialize it after which the two pools operate independently on their respective subtrees When generating cuts the NP module sends the new cuts to the cut pool assigned to service the node during whose processing the cuts were generated The primary motivation behind the idea of multiple cut pools is two fold First we want simply to limit the size of each pool as much as possible By limiting the number of nodes that a cut pool has to service the number of cuts in the pool will be similarly limited This not only allows cut storage to spread over multiple processors and hence increases the available memory but at the same time the efficiency with which the cut pool can be scanned for violated cuts is also increased A secondary reason for maintaining multiple cut pools is that it allows us to limit the scanning of cuts to only those that were generated in the same subtree as the current search node As described above this helps focus the search and should increase the efficiency and effectiveness of the search This idea also allows us to generate locally valid cuts such as the classical Gomory cuts see 28 4 5 Parallelizing BCP Because of the clear partitioning of work that occurs when the branching operation generates new subproblems branch and bound algorithms lend themselves well to parallelization As a result there is already a significant body of research on performing branch and bound in parallel environments We again po
194. on the user is required to construct a description of the subprob lem in column ordered format and pass it back to SYMPHONY by filling out the MIPdesc data structure described in Section 6 3 2 1 The user is not responsible for allocating extra memory to allow for the addition of dynamically generated cuts and variables The arrays allocated in user_create_subproblem are owned by SYMPHONY after allocation and are freed as soon as the relaxation is loaded into the solver However if the user has an idea as to the maximum number of variables and constraints that are likely to be generated during processing of the subproblem this information can be passed to SYMPHONY in the variables maxn maxm and maxnz These numbers are only estimates that SYMPHONY can use to perform memory allocation They do not have to be exact numbers In fact these estimates need not be provided at all The obj_sense and obj_offset fields are set globally in the function user_initialize_root_node see Section6 3 1 Setting them here will have no effect Note that the user should return USER_DEFAULT if an MPS or GMPL AMPL file was read in to describe the original MILP SYMPHONY will allocate the corresponding arrays and specify the constraint matrix automatically in this case Arguments void user IN Pointer to the user defined LP data structure int indices IN The list of the active variables base and extra MIPdesc mip OUT Pointer to the MIPdesc data str
195. on zero ele ments double new_rhs_val IN Array of the values of these non zero elements double ub_for_new_rhs OUT Pointer to a double indicating the lower bound ob tained for the new problem This value will be set to SYM_INFINITY if an upper bound can not be found Return values FUNCTION_TERMINATED_NORMALLY Function invoked successfully FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully 148 gt sym_get_lb_for_new_obj int sym_get_lb_for_new_rhs sym_environment env int cnt int new_obj_ind double new_obj_val double l1b_for_new_obj Description This routine is used for a basic sensitivity analysis of the objective function case It returns a quick lower bound for the problem with a modified objective vector using the information gathered from the branching tree of the original solved problem Note that in order to use this feature the sensitivity analysis parameter needs to be set before solving the original problem Arguments sym_environment env IN int cnt IN int new_obj_ind IN double new_obj_val IN double lb_for_new_obj OUT Return values FUNCTION_TERMINATED_NORMALLY Pointer to the SYMPHONY environment The number of the non zero elements in the new ob jective coefficients Array of the column indices of these non zero ele ments Array of the values of these non zero elements Pointer to a double indicating the lower bound ob tained for the new problem Function
196. ormation including the parallel configuration instructions see the SYMPHONY Applications USER INSTALL file 5 2 2 Microsoft Visual C First download SYMPHONY 5 2 3 and unpack the archive if it is required You now have three options You can either compile on the command line using the automated DEVENV build system or NMAKE utility or you can use the provided project and solution files For all of the following options first go to the SYMPHONY ApplicationslUSERAMSVisualStudio v8 directory 5 2 2 1 Using the MSDEV Utility e Open a command line terminal and type devenv user sln make all This will create both the debug and release versions of USER application If you want to compile only one of them type devenv user sln make all debug or devenv user sln make all release For each command the executable user will be created in Debug and or Release directories e To test the executable type Debug user exe F sample user e If USER source files are modified type devenv user sln make all rebuild in order to clean and rebuild everything 58 5 2 2 2 Using the NMAKE Utility e Edit the file user mak to reflect your environment Only minor edits should be required An explanation of what has to be set is contained in the comments in the makefile This basically requires the same routines that one needs to walk through in SYMPHONY s makefile See the related parts of 2 2 3 4 section of SYMPHONY above e O
197. orted outcome since it is an extreme point on the convex lower envelope of the set of Pareto outcomes Unfortunately not all efficient outcomes are supported so it is not possible to enumerate the set of Pareto outcomes by solving a sequence of ILPs from this parameterized family To obtain all Pareto outcomes one must replace the weighted sum objective 4 3 with an objective based on the weighted Chebyshev norm studied by Eswaran et al 14 and Solanki 34 If x is a solution to a weighted sum problem with a 1 and zf is the solution with a 0 then the weighted Chebyshev norm of a feasible solution p is max a cp cx 1 a dp dx 4 4 Although this objective function is not linear it can easily be linearized by adding an artificial variable resulting in a second parameterized family of ILPs Under the assumption of uniform dominance Bowman showed that an outcome is Pareto if and only if it can be obtained by solving some ILP in this family 5 In 31 the authors presented a method for enumerating all Pareto outcomes by solving a sequence of ILPs in this parameterized family By slightly perturbing the objective function they also showed how to relax the uniform dominance assumption Note that the set of all supported outcomes which can be thought of as an approximation of the set of Pareto outcomes can be similarly obtained by solving a sequence of ILPs with weighted sum objectives SYMPHONY 5 2 3 contains a gener
198. ouble values double ub double etol int num_cuts int alloc_cuts cut_data cuts user_problem prob user_problem user double edge_val 200 200 Matrix of edge values int i jy k cutind 3 double cutval 3 68 int cutnum 0 Allocate the edge_val matrix to zero we could also just calloc it memset char edge_val 0 200 200 ISIZE for i 0 i lt varnum i edge_val prob gt nodel indices i prob gt node2 indices i values i for i 0 i lt prob gt nnodes i for j itl j lt prob gt nnodes jt for k j 1 k lt prob gt nnodes k 4 if edge_val li j edge_val j k edge_val i k gt 1 0 etol Found violated triangle cut Form the cut as a sparse vector cutind 0 prob gt index i j cutind 1 prob gt index j k cutind 2 prob gt index i k cutval 0 cutval 1 cutval 2 1 0 cg_add_explicit_cut 3 cutind cutval 1 0 0 L TRUE num_cuts alloc_cuts cuts cutnum return USER_SUCCESS Note the call of cg_add_explicit_cut which tells SYMPHONY about any cuts found If we now solve the matching problem on the sample data set the number of nodes in the branch and bound tree should just be 1 rather than 3 without cut generation 69 70 Chapter 6 Reference 6 1 Callable Library C API This chapter specifies the interface for using SYMPHONY s callable library These function call
199. ows Explorer e SYMPHONY supports automatic configuration using the new COIN OR build system and the GNU autotools Using the autotools it is now possible to build SYMPHONY in most operating systems and with most common compilers without user intervention e Both the distributed and shared memory parallel configurations are fully implemented tested and supported The user can now build and execute custom SYMPHONY applications in parallel as well as solving generic MILPs in parallel out of the box e There are additional options for warm starting The user can trim the warm starting tree before starting to resolve a problem More specifically the user can decide to initiate warm starting with a predefined partition of the final branch and cut tree resulting from a previous solution procedure This partition can include either a number of nodes created first during the solution procedure or all of the nodes above a given level of the tree e The COIN OR repository the current host of SYMPHONY has recently undergone some significant improvements of its own that have resulted in improved services to users detailed below SYMPHONY has a project management Web site where users can submit trouble tick ets browse the source code interactively and get up to date information on development The address of the new site is https projects coin or org SYMPHONY SYMPHONY is hosted using subversion a version control system with feat
200. p_edgeweights 220 disp_nodelabels 220 disp_nodeweights 220 echo_commands 219 edge_dash 220 edgeweight_font 220 interactive_mode 220 node_dash 220 node_radius 220 nodelabel font 220 nodeweight_font 220 source path 219 viewable_height 220 viewable_width 220 Global Parameters granularity 218 infile name 218 probname 218 random_seed 218 upper_bound 218 verbosity 218 LP Parameters 244 all_cut_timeout 226 base_constraints_always_ effective 226 branch_on_cuts 226 compare_candidates_default 228 cut_pool_check frequency 224 discard_slack_cuts 226 display_solution default 227 do_logical fixing 226 do_reduced_cost_fixing 226 eff_cnt_before_cutpool 225 first_Ip_all_cuts_time_out 226 first lp_first_cut_time_out 226 fixed_to_ub_before_logical_fixing 227 fixed_to_ub_frac_before_logical_fixing 227 gap_as_last_gap_frac 226 gap_as_ub_frac 226 generate_cgl_clique_cuts 228 generate_cgl cuts 228 generate_cgl_flow_and_cover_cuts 228 generate_cgl_gomory_cuts 228 generate_cgl_knapsack_cuts 228 generate_cgl lift_and_project_cuts 228 generate_cgl oddhole_cuts 228 generate_cgl probing_cuts 228 generate_cgl rounding_cuts 228 ineff cnt_to_delete 225 ineffective_constraints 225 is feasible default 227 later_Ip_all_cuts_time_out 226 later_lp_first_cut_time_out 226 LP_verbosity 224 mat_col_compress_num 225 mat_col_compress_ratio 225 mat_row_compress_num 225 mat_row_compress_ratio 225 max
201. r both CHECK_LEVEL_AND_TOUCHES 3 Note that with CHECK_ALL_CUTS set SYMPHONY will still only check the first cuts_to_check cuts in the list ordered by quality see the function user_check_cut cuts_to_check integer 1000 Indicates how many cuts in the pool to actually check The list is ordered by quality and the first cuts_to_check cuts are checked for violation 234 6 4 8 C Interface OSI Parameters As the implementation of the whole interface there exists a matching C interface parameter to each of the C Interface OSI parameter and the parameter setting functions are designed to set the corresponding C interface parameter Thus we will just give a table of the parameter names their C interface complements and the values they can be set to rather than their detailed de scriptions For each parameter the user can see the C interface complement for further explanation C Interface C Interface Value OsiSymVerbosity verbosity user defined OsiSymWarmStart warm start boolean OsiSymNodeLimit OsiMaxNumlteration OsiMaxNumlterationHotStart node_limit user defined OsiSymFindFirstFeasible find first_feasible boolean OsiSymSearchStrategy node_selection_rule LOWEST_LP_FIRST HIGHEST_LP_FIRST BREADTH_FIRST_SEARCH DEPTH_FIRST_SEARCH OsiSymUsePermanentCutPools use_permanent_cut_pools boolean
202. r bound This is useful if the BEST_ESTIMATE diving strategy is used see the treeman ager parameter diving strategy Section 6 4 4 Return values USER_ERROR Error This error is probably not fatal USER_SUCCESS User executed function successfully USER_DEFAULT No action someday there may be a default MIP heuristic here 159 gt user_initialize_root_node int user_initialize_root_node void user int basevarnum int basevars int basecutnum int extravarnum int extravars char obj_sense double obj_offset char colnames int colgen_strat Description In this function the user must specify the list of indices for the base and extra variables The option to specify a variable as base is provided simply for efficiency reasons If there is no reasonable way to come up with a set of base variables then all variables should be specified as extra see Section 4 3 2 1 for a discussion of base and extra variables If the function returns USER DEFAULT and sets extravarnum then SYMPHONY will put all variables indexed from 0 to extravarnum in the set of extra variables by default If an MPS or GMPL AMPL file was read in using SYMPHONY s built in parser i e the default behavior of user_io 6 3 1 was not modified then extravarnum need not be set In this function the user may also specify column names for display purposes If the colnames array is allocated then SYMPHONY will use for displaying solutions If the data wa
203. r name depending on whether the user set the column names See user receive lp data in Section 6 3 2 2 for more discussion Arguments void user IN Pointer to the user defined data structure int msgtag IN FEASIBLE_SOLUTION_NONZEROS or FEASIBLE_SOLUTION_USER The cost of the feasible solution The number of variables whose user indices and values were sent length of indices and values int indices IN The user indices of the nonzero variables double values IN The corresponding values double cost int numvars Return values USER_ERROR Ignored This is probably not a fatal error USER_SUCCESS The solution has been unpacked and stored USER_DEFAULT Store the nonzeros in the solutions for later display 162 gt user_send_lp_data int user_send_lp_data void user void user_1p Description If the user wishes to employ parallelism she has to send all problem specific data that will be needed to implement user functions in the LP module in order to set up the initial LP relaxation and perform later computations This could include instance data as well as user parameter settings see Section 5 5 1 for a discussion of this This is one of the few places where the user may need to worry about the configuration of the modules If either the tree manager or the LP are running as a separate process either COMPILE_IN_LP or COMPILE_IN_TM are FALSE in the make file then the data will be sent and received through message pas
204. r or upper bounds Display user indices as hexadecimals and values of variables not at their lower and upper bounds Wrapper invoked from fathom_branch with DISP_FEAS SOLUTION or DISP_RELAXED_SOLUTION after solving an LP relaxation and checking its feasibil ity status If it was not feasible and no cut could be added either then the wrapper is invoked once more now with DISP_FINAL_RELAXED_SOLUTION 181 gt user_shall we_branch int user_shall_we_branch void user double lpetol int cutnun int slacks_in_matrix_num cut_data slacks_in_matrix int slack_cut_num cut_data slack_cuts int varnum var_desc vars double x char status int cand_num branch_obj candidates int action Description There are two user written functions invoked from select_candidates_u The first one user_shall_we_branch decides whether to branch at all the second one user_select_candidates chooses the branching objects The argument lists of the two functions are the same and if branching occurs see discussion below then the contents of cand_num and candidates will not change between the calls to the two functions The first of these two functions is invoked in each iteration after solving the LP relaxation and possibly generating cuts Therefore by the time it is called some violated cuts might be known Still the user might decide to branch anyway The second function is invoked only when branching is decided on Giv
205. raint matrix writeMps write the current problem in MPS format applyRowCut add some row cuts applyColCut add some column cuts Sym WarmStart warm start_desc sym_create_copy warm_start create a SYMPHONY warm start by copying the given one SymWarmStart char sym_read warm start create a SYMPHONY warm start reading from file getCopyOfWarmStartDesc sym_create_copy_warm_start get the copy of the warm start structure writeToFile sym_write warm _start_desc write the loaded warm start to a file 153 6 3 User Callback API 6 3 1 Master module callbacks gt user_usage void user_usage Description SYMPHONY s command line switches are all lower case letters The user can use any upper case letter except H and as specified below for command line switches to con trol user defined parameter settings without the use of a parameter file The function user_usage can optionally print out usage information for the user defined command line switches The command line switch H automatically calls the user s usage sub routine The switch h prints SYMPHONY s own usage information In its default configuration the command line switch F is used to specify the file in which the in stance data is contained either an MPS file or an GMPL AMPL file The D switch is used to specify the data file if an GMPL AMPL file is being read in see the README file T
206. results have been re discovered in light of their new found computational importance Finally the use of parallel computing has allowed researchers to further leverage their gains Because of the rapidly increasing sophistication of computational techniques one of the main dif ficulties faced by researchers who wish to apply these techniques is the level of effort required to develop an efficient implementation The inherent need for incorporating problem dependent methods most notably for dynamic generation of variables and cutting planes has typically re quired the time consuming development of custom implementations Around 1993 this led to the development by two independent research groups of software libraries aimed at providing a generic framework that users could easily customize for use in a particular problem setting One of these groups headed by Jiinger and Thienel eventually produced ABACUS A Branch And CUt Sys tem 21 while the other headed by the authors produced what was then known as COMPSys Combinatorial Optimization Multi processing System After several revisions to enable more broad functionality COMPSys became SYMPHONY Single or Multi Process Optimization over Networks A version of SYMPHONY written in C which we call COIN BCP has also been produced at IBM under the COIN OR project 24 The COIN BCP package takes substantially the 3 same approach and has the same functionality as SYMPHONY but has exte
207. ria ILP is a generalization of a standard ILP presented earlier that includes a second objective function yielding an optimization problem of the form vmin cx dz s t Ax 4 2 MIA b vis The operator umin is understood to mean that solving this program is the problem of generating efficient solutions which are these feasible solutions p to 4 2 for which there does not exist a second distinct feasible solution q such that cq lt cp and dq lt dp and at least one inequality is strict Note that 4 2 does not have a unique optimal solution value but a set of pairs of solution values called outcomes The pairs of solution values corresponding to efficient solutions are called Pareto outcomes Surveys of methodology for for enumerating the Pareto outcomes of multicriteria integer programs are provided by Climaco et al 7 and more recently by Ehrgott and Gandibleux 11 12 and Ehrgott and Wiecek 13 The bicriteria ILP 4 2 can be converted to a standard ILP by taking a nonnegative linear combi nation of the objective functions 17 Without loss of generality the weights can be scaled so they sum to one resulting in a family of ILPs parameterized by a scalar 0 lt a lt 1 with the bicriteria objective function replaced by the weighted sum objective ac 1 a d z 4 3 47 Each selection of weight produces a different single objective problem Solving the resulting ILP produces a Pareto outcome called a supp
208. row A closely related data structure is the waiting_row essentially the unpacked form of a cut There are six fields source_pid Used internally by SYMPHONY cut_data cut Pointer to the cut from which the row was generated int nzcnt matind matval Fields describing the row nzcnt is the number of nonze ros in the row i e the length of the matind and matval arrays which are the variable indices wrt the current LP relaxation and nonzero coefficients in the row double violation Ifthe constraint corresponding to the cut is violated this value contains the degree of violation the absolute value of the difference between the row activity level i e lhs and the right hand side This value does not have to be set by the user 173 gt var_desc The var_desc structure is used list the variables in the current relaxation There are four fields int userind The user index of the variables int colind The column index of the variables in the current relaxation double 1b The lower bound of the variable double ub The upper bound of the variable 174 6 3 2 2 Function Descriptions Now we describe the functions themselves gt user_receive_lp_data int user_receive_lp_data void user Description This function only has to be filled out for parallel execution and only if either the TM or LP modules are configured as separate processes Otherwise data will have been copied
209. rrent global upper bound The current error tolerance in the LP Pointer to the number of cuts generated and sent to the LP USER_SUCCESS The user function exited properly USER_DEFAULT No cuts were generated Invoked from Whenever an LP solution is received 207 gt user_check_validity_of_cut int user_check_validity_of_cut void user cut_data new_cut Description This function is provided as a debugging tool Every cut that is to be sent to the LP solver is first passed to this function where the user can independently verify that the cut is valid by testing it against a known feasible solution usually an optimal one This is useful for determining why a particular known feasible optimal solution was never found Usually this is due to an invalid cut being added See Section 5 6 3 for more on this feature Arguments void user IN Pointer to the user defined data structure cut_data new_cut IN Pointer to the cut that must be checked Return values USER_ERROR Ignored USER_SUCCESS The user is done checking the cut USER_DEFAULT The cut is ignored Invoked from Whenever a cut is being sent to the LP 208 gt user_free_cg int user_free_cg void user Description The user has to free all the data structures within user and also free user itself The user can use the built in macro FREE that checks the existence of a pointer before freeing 16 Arguments void user INOUT Pointer to the user defi
210. rs rel _br_threshold integer 8 It is assumed that the score obtained by branching on a given variable these many times is reliable for estimating the pseudocosts of this variable in the rest of the branch and bound algorithm In other words if reliability branching is enabled strong branching is used on a variable at most rel br threshold many times rel br max solves integer 20 If reliability branching is enabled this parameter determines the maximum number of strong branching LPs that are solved in each node If some branching candidates have reliable estimates the number of LPs can be less than the value of this parameter rel br_cand threshold integer 10 If reliability branching is enabled then strong branching is stopped if the last rel_br_cand_threshold LPs did not give a better improvement in the lower bound is feasible default integer TEST_INTEGRALITY 1 Determines the default test to be used to determine feasibility This parameter is provided so that the user can change the default behavior without recompiling The only other option is TEST_ZERO_ONE 0 send feasible solution default integer SEND_NONZEROS O Determines the form in which to send the feasible solution This parameter is provided so that the user can change the default behavior without recompiling This is currently the only option send_lp_solution_default integer SEND_NONZEROS O Determines the default form in which to se
211. rsion 5 0 In particular Ashutosh and Menal did all of the work that went into improving SYMPHONY for release 5 2 I would also like to thank Matthew Galati and Ondrej Medek who contributed to the development of SYMPHONY over the years Finally my sincere appreciation goes to Leslie Trotter William Cook Cornell University Rice University Lehigh University the National Science Foundation and the state of Texas all of whom have supported the development of SYMPHONY Contents 1 Introduction 11 Introducing SYMPHONY PA es s sod agosa a ee Ee Ee Re G LE Whate NEW ss s o aota aa ek a A AA A i 1 A Briet Mistery lt o e ee cee ab e or A ee E E eRe ee eS 14 Related Wark aa hele a ee a E a Ra ARA A Be 15 How to Use This Manmmal ceso ed 8 eed eo ee eee dee A 1 6 Getting Additional Help e sa eaa e e a w teea a de Dee A 2 Installing SYMPHONY 2 1 Installing the Binary Distribution o s s a sosa osa a soau ara Sa aoi aoa ee 2 1 1 Installation in Unix like environments a 2 1 2 Installation for Use With Microsoft VisualC 22 Building From Source o sa s aca Ee a gk ah Rede a eee ag a Se ee ee a 2 2 1 External Dependencies o sarasa damp aa sataa dmeas gii 2 2 2 Building in Unix like environments saoao e 2 2 3 Building Using Microsoft Visual C oaaao aaae 2 2 4 Building SYMPHONY Applications o aae eaaa 3 Using SYMPHONY 3 1 Using SYMPHONY Interactively gt o ss ce sacca a
212. s USER_SUCCESS User I O was completed successfully USER DEFAULT Input will be read in from an MPS or GMPL AMPL file 157 gt user_init_draw_graph int user_init_draw_graph void user int dg_id Description This function is invoked only if the do_draw graph parameter is set The user can initialize the graph drawing module by sending some initial information e g the location of the nodes of a graph like in the TSP Arguments void user IN Pointer to the user defined data structure int dg id IN The process id of the graph drawing module Return values USER_ERROR Error SYMPHONY stops USER_SUCCESS The user completed initialization successfully USER_DEFAULT No action 158 gt user_start_heurs int user_start_heurs void user double ub double ub_estimate Description The user invokes heuristics and generates the initial global upper bound and also perhaps an upper bound estimate This is the last place where the user can do things before the branch and cut algorithm starts She might do some preprocessing in addition to generating the upper bound Arguments void user IN Pointer to the user defined data structure double ub OUT Pointer to the global upper bound Initially the upper bound is set to either MAXDOUBLE or the bound read in from the pa rameter file and should be changed by the user only if a better valid upper bound is found double ub_estimate OUT Pointer to an estimate of the global uppe
213. s void user INOUT Pointer to the user defined data structure int text_length IN The length of text char text IN int owner_tid IN The tid of the process that initiated this window Return values USER_ERROR Error Ignored USER_SUCCESS The user successfully interpreted the text 219 6 4 Run time Parameters Parameters can be set in one of two ways Some commonly used parameters can be set on the command line To see a list of these run SYMPHONY with no command line arguments Other parameters must be set in a parameter file The name of this file is specified on the command line with f Each line of the parameter file contains either a comment or two words a keyword and a value separated by white space If the first word sequence of non white space characters on a line is not a keyword then the line is considered a comment line Otherwise the parameter corresponding to the keyword is set to the listed value Usually the keyword is the same as the parameter name in the source code Here we list the keywords the type of value that should be given with the keywords and the default value A parameter corresponding to keyword K in module P can also be set by using the keyword P_K To make this list shorter occasionally a comma separated list of parameters is given if the meanings of those parameters are strongly connected For clarity the constant name is sometimes given instead of the numerical value for d
214. s can be used to build custom applications that call SYMPHONY as a subroutine as described in Section 3 3 All callable library function begin with the prefix sym_ To call these function from an application include the header file symphony h and then link with the SYMPHONY library as described in Section 2 In general if an array is requested such as the array of lower bounds on the variables for instance the user is responsible for allocating an array of appropriate size and passing it to SYMPHONY SYMPHONY will then fill up the array 71 6 1 1 Primary Interface Functions gt sym_open environment sym_environment sym_open_environment Description This routine is used to get a new SYMPHONY environment to be passed as an ar gument to all other API subroutines This routine also invokes the callback function user_initialize see Section 6 3 1 Return values NULL Error Environment could not be initialized None of the other API subroutines can be called after this point sym_environment Pointer to a successfully opened environment 72 gt sym_create_copy_ environment sym_environment sym_create_copy_environment sym_environment env Description This routine is used to copy the given environment Arguments sym_environment env IN Pointer to the SYMPHONY environment Return values NULL An empty environment is passed in SYM_ENVIRONMENT Pointer to the copy of the environment 73 gt sym_parse_command_l
215. s at non zero level If the user packs the solution herself then the same data must be packed here that will be received in the user_receive feasible solution function in the master module See the description of that function for details This function will only be called when either the LP or tree manager are running as a separate executable Otherwise the solution gets stored within the LP user data structure Arguments void user double lpetol int varnum int indices double values Return values USER_ERROR USER_SUCCESS USER_DEFAULT SEND_NONZEROS IN Pointer to the user defined LP data structure The e tolerance of the LP solver The length of the indices and values arrays User indices of variables at nonzero level in the current solu tion Values of the variables listed in indices Error Do the default User packed the solution Regulated by the parameter pack feasible solution default but set to SEND_NONZEROS unless overridden by the user Pack the nonzero values and their indices Wrapper invoked as soon as feasibility is detected anywhere 180 gt user_display_lp_solution int user_display_lp_solution void user int which_sol Description int varnum int indices double values Given a solution to an LP relaxation the indices and values of the nonzero variables the user can graphically display it The which_sol argument shows what kind of solution is passed to the function
216. s ga ek a a eee ia ee eee 233 6 4 8 C Interface OSI Parameters e eee 235 Bibliography 237 Index 241 viii Chapter 1 Introduction 1 1 Introducing SYMPHONY 5 2 3 Welcome to the SYMPHONY Version 5 2 3 user s manual Whether you are a new user or simply upgrading this manual will help you get started with what we hope you will find to be a useful and powerful framework for solving mixed integer linear programs MILP sequentially or in par allel The subroutines in the SYMPHONY library comprise a state of the art MILP solver with a modular design that makes it easy to customize for various problem settings SYMPHONY works out of the box as a generic MILP solver that can be invoked from the command line through an interactive shell or by linking to the provided callable library which has both C and C inter faces with a look and feel similar to that of other popular solvers see Sections 6 1 and 6 2 for the library routines Models can be read in MPS or GMPL a subset of AMPL format as well as by interfacing with more powerful modeling environments such as FlopC also provided with the distribution To develop a customized SYMPHONY application various callbacks can be written and parameters set that modify the default behavior of the algorithm Section 3 4 contains an overview of the API for these subroutines Files containing function stubs are provided with the distribution SYMPHONY can be built on almost an
217. s is used in all lower levels of the tree strong branching high _1ow weight double 0 8 This parameter is used to calculate the score of each branching candidate The candidate with the highest score is then selected for branching Let ae 2 be the estimated change in objective function value when we branch on the candidate i Then the score of candidate i is s ax min z7 2 1 a xmax z7 z7 where a is the value of strong branching high low weight This value should always lie in the interval 0 1 use_hot_starts boolean TRUE Determines if the LP solver is asked to make special arrange ments for doing dual simplex iterations when bounds on a variable are changed for strong branching Some LP solvers provide such options so that strong branching can be performed much faster than the regular dual simplex procedure 230 should_use_rel_br boolean TRUE Determines if reliability braching is used to determine branching candidates or not This parameter is set to FALSE if OPENMP is used When this branching rule is disabled strong branching is used to select a candidate rel_br_override_ default boolean TRUE If reliability branching is enabled and this paramter is set to TRUE then the policy of selecting branching candidates is automatically adjusted on the basis of bounds on solution value and the time elapsed If this parameter is set to FALSE the policy is based on the values of the following three paramete
218. s needed Receive a subproblem and process it in conjunction with the cut generator and the cut pool Decide which cuts should be sent to the global pool to be made available to other NP modules If necessary choose a branching object and send its description back to the tree manager Perform the fathoming operation including generating variables 43 4 3 3 4 The Cut Generation Module The cut generator performs only one function generating valid inequalities violated by the current fractional solution and sending them back to the requesting LP process Here are the functions performed by the cut generator module e Receive an LP solution and attempt to separate it from the convex hull of all solutions e Send generated valid inequalities back to the NP module e When finished processing a solution vector inform the NP module not to expect any more cuts in case it is still waiting 4 3 3 5 The Cut Management Module The concept of a cut pool was first suggested by Padberg and Rinaldi 29 and is based on the observation that in BCP the inequalities which are generated while processing a particular node in the search tree are also generally valid and potentially useful at other nodes Since generating these cuts is usually a relatively expensive operation the cut pool maintains a list of the best or strongest cuts found in the tree so far for use in processing future subproblems Hence the cut manager functions as an auxil
219. s read in from either an MPS or GMPL AMPL file then the column names will be set automatically Arguments void user IN Pointer to the user defined data structure int basevarnum OUT Pointer to the number of base variables int basevars OUT Pointer to an array containing a list of user indices of the base variables to be active in the root int basecutnum OUT The number of base constraints int extravarnum OUT Pointer to the number of extra active variables in the root int extravars OUT Pointer to an array containing a list of user indices of the extra variables to be active in the root char obj_sense INOUT Whether to negate the objective function value when printing the solution set to either MAXIMIZE or MINIMIZE Note that SYMPHONY always minimizes this only effects the printing of the solution The default is MINIMIZE double obj_offset INOUT A specified constant to be added to the objective func tion value when printing out the solution int colnames OUT Pointer to an array containing a list of column names to be used for display purposes int colgen_strat INOUT The default strategy or one that has been read in from the parameter file is passed in but the user is free to change it See colgen_strat in the description of pa rameters for details on how to set it Return values 160 USER_ERROR Error SYMPHONY stops USER_SUCCESS The required data are filled in USER DEFAULT All variables indexed 0
220. sing See user receive lp data in Section 6 3 2 2 for more discussion Otherwise it can be copied through shared memory The easiest solution which is set up by default is to simply copy over a pointer to a single user data structure where instance data is stored The code for the two cases is put in the same source file by use of ifdef statements See the comments in the code stub for this function for more details Arguments void user IN Pointer to the user defined data structure void user_1p OUT Pointer to the user defined data structure for the LP mod ule Return values USER_ERROR Error SYMPHONY stops USER_SUCCESS Packing is done USER_DEFAULT User has no data to send This would be used when SYM PHONY has read in an MPS or GMPL AMPL model file 163 gt user_send_cg_data int user_pack_cg_data void user void user_cg Description If the user wishes to employ parallelism and wants a separate cut generator module this function can be used to send all problem specific data that will be needed by the cut generator module to perform separation This could include instance data as well as user parameter settings see Section 5 5 1 for a discussion of this This is one of the few places where the user may need to worry about the configuration of the mod ules If either the tree manager or the LP are running as a separate process either COMPILE_IN_LP or COMPILE_IN_TM are FALSE in the make file then the data will be sen
221. sis Boston MA 2004 Kluwer Academic Publishers P K Eswaran A Ravindran and H Moskowitz Algorithms for nonlinear integer bicriterion problems Journal of Optimization Theory and Applications 63 2 261 279 1989 A Geist A Beguelin J Dongarra W Jiang R Manchek and V Sunderam PVM Parallel Virtual Machine The MIT Press Cambridge MA 1994 B Gendron and T G Crainic Parallel branch and bound algorithms Survey and synthesis Operations Research 42 1042 1066 1994 A M Geoffrion Proper efficiency and the theory of vector maximization Journal of Mathe matical Analysis and Applications 22 618 630 1968 A Y Grama and V Kumar State of the art in parallel search techniques for discrete opti mization problems EEE Transactions on Knowledge and Data Engineering 11 1 8 1999 M Gr tschel M J nger and G Reinelt A cutting plane algorithm for the linear ordering problem Operations Research 32 6 1195 1220 1984 K Hoffman and M Padberg LP based combinatorial problem solving Annals of Operations Research 4 145 194 1985 M J nger and S Thienel The ABACUS system for branch and cut and price algorithms in integer programming and combinatorial optimization Software Practice and Experience 30 1325 1352 2001 V Kumar and V N Rao Parallel depth first search part II Analysis International Journal of Parallel Programming 16 501 519 1987 J Linderoth Topics in Parallel Integer Optimiza
222. sis with SYMPHONY s bicriteria solver 31 int main int argc char argv OsiSymSolverInterface si si parseCommandLine argc argv si loadProblem si branchAndBound J Figure 3 5 Implementation of a generic MILP solver with the SYMPHONY OSI interface SYMPHONY to be accessed through the OSI including customized solvers and those configured to run on parallel architectures To ease code maintenance for each method in the OSI base class there is a corresponding method in the callable library The OSI methods are implemented simply as wrapped calls to the SYMPHONY callable library When an instance of the OSI interface class is constructed a call is made to sym_open_environment and a pointer to the environment is stored in the class Most subsequent calls within the class can then be made without any arguments When the OSI object is destroyed sym_close_environment is called and the environment is destroyed To fully support SYMPHONY s capabilities we have extended the OSI interface to include some methods not in the base class For example we added calls equivalent to our sym_parse_command_line and sym_find_initial_bounds Figure 3 5 shows the program of Figure 3 1 implemented using the OSI interface Note that the code would be exactly the same for accessing any customized SYMPHONY solver sequential or parallel Although we are using the OSI to access a MILP solver the current version of the OSI is geared
223. splay_lp_solution 179 user_display_solution 164 user find cuts 204 user_finished_checking_cuts 212 user_free_cg 207 user_free_cp 213 user_free_lp 201 user free master 167 user_generate_column 195 user_generate_cuts_in_lp 197 user_init_draw_graph 156 user_initialize 153 user initialize_root_node 158 user_interpret_text 217 user_io 155 user_is_feasible 176 user_logical_fixing 194 user_prepare_to_check_cuts 210 user_print_branch stat 188 user_print_stat_on_cuts_added 199 user_process_own_messages 166 user_purge_waiting_rows 200 user _readparams 154 user receive cg data 202 user_receive_cp_data 208 user_receive_feasible_solution 160 user_receive lp_data 173 user_receive_lp_solution_cg 203 user_receive lp_solution_cp 209 user_same_cuts 190 user_select_candidates 183 user_select_child 187 user_send_cg_data 162 user_send_cp_data 163 user_send_feas_sol 165 user_send_feasible_solution 178 user_send_lp_data 161 user_send_Ip _solution 193 user_shall_we_branch 180 user_start_heurs 157 user_unpack_cuts 191 user_usage 152 243 Index of Parameters Cut Generator Parameters CG_verbosity 228 Cut Pool Parameters block_size 229 check which 229 cp logging 229 CP_verbosity 229 cp_warm_start 229 cuts_to_check 229 delete_which 229 max_number_of_cuts 229 max_size 229 min_to_delete 229 touches_until_deletion 229 Draw Graph Parameters canvas_height 219 canvas_width 219 dis
224. t and received through message passing See user receive cg data in Section 6 3 3 for more discussion Otherwise it can be copied through shared memory The easiest solution which is set up by default is to simply copy over a pointer to a single user data structure where instance data is stored The code for the two cases is put in the same source file by use of ifdef statements See the comments in the code stub for this function for more details Arguments void user IN Pointer to the user defined data structure void xuser_cg OUT Pointer to the user defined data structure for the cut gen erator module Return values USER_ERROR Error SYMPHONY stops USER_SUCCESS Packing is done USER_DEFAULT No data to send to the cut generator no separation performed 164 gt user_send_cp_data int user_pack_cp_data void user void user_cp Description If the user wishes to employ parallelism and wants to use the cut pool to store user defined cuts this function can be used to send all problem specific data that will be needed by the cut pool module This could include instance data as well as user pa rameter settings see Section 5 5 1 for a discussion of this This is one of the few places where the user may need to worry about the configuration of the modules If either the tree manager or the LP are running as a separate process either COMPILE_IN_LP or COMPILE_IN_TM are FALSE in the make file then the data will be sent and rece
225. t generate_what int cutnun cut_data cuts int prevind int nextind int real_nextind double colval int colind int collen double obj double lb double ub Description This function is called when pricing out the columns that are not already fixed and are not explicitly represented in the matrix Only the user knows the explicit description of these columns When a missing variable need to be priced the user is asked to provide the corresponding column SYMPHONY scans through the known variables in the order of their user indices After testing a variable in the matrix prevind SYMPHONY asks the user if there are any missing variables to be priced before the next variable in the matrix nextind If there are missing variables before nextind the user has to supply the user index of the real next variable real_nextind along with the corresponding column Occasionally SYMPHONY asks the user to simply supply the column corresponding to nextind The generate_what flag is used for making a distinction between the two cases in the former case it is set to GENERATE_REAL_NEXTIND and in the latter it is set to GENERATE_NEXTIND 197 Arguments void user int generate _ what int cutnum cut_data x cuts int prevind int nextind int real_nextind double colval int colind int collen double obj double lb double ub Return values USER_ERROR USER_SUCCESS IN IN IN IN IN IN OUT
226. t or not The matind fields of the rows must be filled with indices with respect to the position of the variables in vars Warning For each row the user must make sure that the cut the row was generated from and can be uniquely regenerated from if needed later is safely stored in the waiting_row structure SYMPHONY will free the entries in cuts after this function returns If a row is generated from a cut in cuts and not from a lifted cut the user has the option of physically copying the cut into the corresponding part of the waiting_row structure or copying the pointer to the cut into the waiting_row structure and erasing the pointer in cuts If a row is generated from a lifted cut the user should store a copy of the lifted cut in the corresponding part of waiting_row 194 gt user_send_lp_solution int user_send_lp_solution void user int varnum var_desc vars double x int where Description This function is only used in the case of parallel execution The user has the option to send the LP solution to either the cut pool or the cut generator in some user defined form if desired There are two default options sending the indices and values for all nonzero variables SEND_NONZEROS and sending the indices and values for all fractional variables SEND_FRACTIONS Arguments void user IN Pointer to the user defined LP data structure int varnum IN The number of variables currently in the LP relaxation The length of
227. t sym_load_problem sym_environment env Description This routine loads the description of the problem given in MPS or GMPL AMPL for mat or in a file read by a custom file parser implemented in the user_io see Section 6 3 1 callback If the problem is to be loaded from an MPS or a GMPL AMPL file whose location is specified on the command line then the sym_parse_command_line function has to be invoked beforehand This function also invokes the user callback user initialize root node see Section 6 3 1 Note that if the user wishes to load the problem manually without implementing a callback or using one of SYM PHONY s built in parsers as is typically done in other callable libraries then the sym_explicit_load_problem routine should be used Arguments sym_environment env INOUT Pointer to the SYMPHONY environment Return values ERROR__USER Error User error detected in one of user_io and user_init_draw functions ERROR_ READING_GMPL_FILE Error detected in the given GMPL AMPL file FUNCTION_TERMINATED_ABNORMALLY Function invoked unsuccessfully FUNCTION TERMINATED NORMALLY Function invoked successfully 76 gt sym_explicit_load_problem int sym_explicit_load_problem_user sym_environment env int numcols int numrows int start int index double value double collb double colub char is_int double obj double obj2 char rowsen double rowrhs double rowrng char make_copy Description Th
228. t_child_default integer PREFER_LOWER_OBJ_VALUE 0O Determines the default rule for selecting the child to be processed next For other possible values see the description user_select_child This parameter is provided so that the user can change the default behavior without recompiling mc_find_supported_solutions boolean FALSE By default sym_mc_solve routine will find all the non dominated solutions if the problem to be solved is a bicriteria problem However if the user plans to find only the supported solutions then this parameter has to be set before calling sym_mc_solve routine mc_rho double 0 00001 The value used in augmented Chebyshev norm during the bicriteria solution procedure generate_cgl_cuts boolean TRUE Whether or not to generate cuts using COIN s cut gen eration library Note that to use CGL cuts OSI interface has to be used and moreover the corresponding flags have to be set during installation See the makefile for more details generate_cgl_gomory_cuts boolean TRUE Whether or not to generate Gomory cuts using COIN s cut generation library generate_cgl_knapsack_cuts boolean TRUE Whether or not to generate knapsack cover cuts using COIN s cut generation library generate cgl oddhole cuts boolean TRUE Whether or not to generate generalized odd hole cuts using COIN s cut generation library generate_cgl_probing cuts boolean TRUE Whether or not to generate
229. t_time out first_lp_all_cuts_time_out later_lp_first_cut_time_out later 1p all cuts time out double 0 0 5 1 The next group of parameters determines when the LP should give up waiting for cuts from the cut generator and start to solve the relaxation in its current form or possibly branch if necessary There are two factors that contribute to determining this timeout First is whether this is the first LP in the search node of whether it is a later LP Second is whether any cuts have been added already in this iteration The four timeout parameters correspond to the four possible combinations of these two variables no_cut_timeout This keyword does not have an associated value If this keyword appears on a line by itself or with a value this tells the framework not to time out while waiting for cuts This is useful for debugging since it enables runs with a single LP module to be duplicated 229 all_cut_timeout double no default This keyword tells the framework to set all of the above timeout parameters to the value indicated max_cut_num_per_iter integer 20 The maximum number of cuts that can be added to the LP in an iteration The remaining cuts stay in the local pool to be added in subsequent iterations if they are strong enough do_reduced_cost_fixing boolean FALSE Whether or not to attempt to fix variables by re duced cost This option is highly recommended gap_as_ub_frac gap_as_last_gap_frac
230. ta user_send_cp_data user_receive feasible solution user display _solution user_process_own_messages functions Tree Manager TM found the optimal solution and stopped TM stopped after reaching the predefined time limit TM stopped after reaching the predefined node limit TM stopped after achieving the predefined target gap TM stopped after finding the first feasible solution Error TM stopped User didn t select branching candidate in user_select_candidates callback Error TM stopped after getting an invalid return code Error TM stopped due to some numerical difficul ties Error TM stopped due to communication error Error TM stopped User error detected in one of user callbacks called during TM processes gt sym_is_proven_optimal int sym_is_proven_optimal sym_environment env Description This post solution query routine is used to learn whether the problem was solved to optimality Arguments sym_ environment env IN Pointer to the SYMPHONY environment Return values TRUE The problem was solved to optimality FALSE The problem was not solved to optimality 95 gt sym_is_proven_primal_infeasible int sym_is_proven_primal_infeasible sym_environment env Description This post solution query routine is used to learn whether the problem was proven to be infeasible Arguments sym_ environment env IN Pointer to the SYMPHONY environment Return values TRUE The problem was prove
231. tion PhD thesis School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA 1998 R Lougee Heimer The Common Optimization INterface for Operations Research BM Journal of Research and Development 47 57 66 2003 A Makhorin Introduction to GLPK 2004 Available from http www gnu org software glpk glpk html A Martin Integer programs with block structure Habilitation Thesis Technical University of Berlin Berlin Germany 1998 G L Nemhauser M W P Savelsbergh and G S Sigismondi MINTO a Mixed INTeger Opti mizer Operations Research Letters 15 47 58 1994 G L Nemhauser and L A Wolsey Integer and Combinatorial Optimization Wiley New York 1988 M W Padberg and G Rinaldi A branch and cut algorithm for the solution of large scale traveling salesman problems SIAM Review 33 60 100 1991 238 30 31 32 33 34 35 36 T K Ralphs and L Lad nyi SYMPHONY Version 4 0 User s Manual 2004 http www brandandcut org T K Ralphs M J Saltzman and M M Wiecek An improved algorithm for biobjective integer programming To appear in Annals of Operations Research 2005 V N Rao and V Kumar Parallel depth first search part I Implementation International Journal of Parallel Programming 16 479 499 1987 Y Shinano K Harada and R Hirabayashi A generalized utility for parallel branch and bound algorithms In Proceedings of the 1995 Sevent
232. to SYMPHONY The template code already provides basic command line options for the user The F flag is used to specify the location of a data file from which we will read in the data The datafile contains first the number of nodes in the graph nnodes followed by the pairwise cost matrix nnode by nnode We read the file in with the match_read_data routine in user_main c int match_read_data user_problem prob char infile int i j FILE f NULL if f fopen infile r NULL printf main user file s can t be opened n infile return ERROR__USER Read in the costs fscanf f 4d amp prob gt numnodes for i 0 i lt prob gt numnodes i for j 0 j lt prob gt numnodes j fscanf f d amp prob gt cost i j return FUNCTION_TERMINATED_NORMALLY We can now construct the integer program itself This is done by specifying the constraint matrix and the rim vectors in sparse format We will have a variable for each possible assignment i 7 with 7 lt j We have a constraint for each node i so it can only me matched to one other node We define the IP in our other helper function match_load_problem in user main c In the first part of this routine we will build a description of the IP and then in the second part we will load this representation to SYMPHONY through sym_explicit_load_problem Note that we could instead create a description of each subproblem d
233. ucture int maxn OUT Estimated maximum number of variables int maxm OUT Estimated maximum number of constraints int maxnz OUT Estimated maximum number of nonzeros Return values USER_ERROR Error The LP module is aborted USER_SUCCESS User created the constraint matrix with the base constraints USER_DEFAULT This return code is used when the default routines for reading in an MPS or AMPL file were used and the user wants to let SYMPHONY set up the subproblem automatically This return will cause an error if the default I O routines were not used 176 Post processing The extra constraints are added to the matrix by calling the user_unpack_cuts sub routine and then adding the corresponding rows to the matrix This is easier for the user to implement but less efficient than adding the cuts at the time the original matrix was being constructed Wrapper invoked from process_chain which is invoked when setting up a the initial search node in a chain 177 gt user_is_feasible int user_is_feasible void user double lpetol int varnum int indices double values int feasible double objval int branching double heur_solution Description The user can test the feasibility status of the solution to the current LP relaxation and or return a feasible solution generated by a primal heuristic This function is primarily used in cases where a solution satisfying integrality restrictions may not be feasible i e it m
234. uild instructions in Section 2 1 1 to build the code Note that this will also compile the sequential version Make sure there are links from your PVM_ROOT bin PVM_ARCH subdirectory to each of the executables in your bin directory as required by PVM In order to keep track of the various possible configurations executable and their corresponding libraries are named as follows The name of each executable is symphony along with a combination of the abbreviated names of the modules that were combined to produce that executable joined by underscores m for the master module tm for the tree management module 1p for the node processing module cg for the cut generation module and cp for the cut management module For instance in the default distributed configuration the executables produced are symphony_m_tm_cp and symphony_1p cg To test the parallel version first start the PVM daemon by typing pvm on the command line and then typing quit As above invoke SYMPHONY using the sample MPS file called sample mps included with the distribution To specify the file name use the F command line option i e in the root directory type bin symphony_m_EXT F SYMPHONY Datasets sample mps where EXT is the extension to be added according to the chosen configuration of the modules 2 2 2 5 Building SYMPHONY Applications There are a number of sample applications available as examples of how to do customized devel opment with SYMPHONY These include
235. uld also be with the checking routine To enable this functionality the user must configure SYMPHONY with the flag enable cut check see Section 2 2 2 2 Tracing the optimal path can alert the user when the subproblem which admits a particular known feasible solution at least according to the branching restrictions that have been imposed so far is pruned This could be due to an invalid branching Note that this option currently only works for branching on binary variables To use this facility the user must fill in the function user send feas sol see Section 6 3 1 All that is required is to pass out an array of user indices that are in the feasible solution that you want to trace Each time the subproblem which admits this feasible solution is branched on the branch that continues to admit the solution is marked When one of these marked subproblems is pruned the user is notified To enable this functionality the user must configure SYMPHONY with the flag enable trace path see Sec tion 2 2 2 2 5 6 4 Using the Interactive Graph Drawing Software The Interactive Graph Drawing IGD software package is included with SYMPHONY and SYM PHONY facilitates its use through interfaces with the package The package which is a Tcl Tk application is extremely useful for developing and debugging applications involving graph based problems Given display coordinates for each node in the graph IGD can display support graphs corresponding to fr
236. uld perform in comparison to a problem specific or hardware specific implementation written from scratch It is important to point out that achieving such design goals involves a number of very difficult tradeoffs For instance ease of use is quite often at odds with efficiency In several instances we had to give up some efficiency to make the code easy to work with and to maintain a true black box implementation Maintaining portability across a wide variety of hardware both sequential and parallel also required some difficult choices For example solving large scale problems on sequential platforms requires extremely memory efficient data structures in order to maintain the very large search trees that can be generated These storage schemes however are highly centralized and do not scale well to large numbers of processors 4 3 1 An Object oriented Approach As we have already alluded to applying BCP to large scale problems presents several difficult challenges First and foremost is designing methods and data structures capable of handling the potentially huge numbers of cuts and variables that need to be accounted for during the solution process The dynamic nature of the algorithm requires that we must also be able to efficiently move cuts and variables in and out of the active set of each search node at any time A second closely related challenge is that of effectively dealing with the very large search trees that can be generat
237. umber of objects cuts and variables that are active in each subproblem Keeping this active set as small as possible is one of the keys to efficiently implementing BCP For this reason we chose data structures that enhance our ability to efficiently move objects in and out of the active set Allowing sets of cuts and variables to move in and out of the linear programs simultaneously is one of the most significant challenges of BCP We do this by maintaining an abstract representation of each global object that contains information about how to add it to a particular LP relaxation In the literature on linear and integer programming the terms cut and row are typically used interchangeably Similarly variable and column are often used with similar meanings In many situations this is appropriate and does not cause confusion However in object oriented BCP frameworks such as SYMPHONY or ABACUS 21 a cut and a row are fundamentally different objects A cut also referred to as a constraint is a user defined representation of an abstract object which can only be realized as a row in an LP matrix with respect to a particular set of active variables Similarly a variable is a representation which can only be realized as a column of an LP matrix with respect to a particular set of cuts This distinction between the representation and the realization of objects is a crucial design element and is what allows us to effectively address some of the challenges
238. unction invoked unsuccessfully FUNCTION_TERMINATED_NORMALLY Function invoked successfully 87 gt sym_set_int_param void sym_set_int_param sym_environment env char key int value Description This routine is used to set an integer type parameter Arguments sym_environment env INOUT Pointer to the SYMPHONY environment char key IN The name of the parameter to be set int value OUT New value of the corresponding parameter 88 gt sym_set_dbl_param void sym_set_int_param sym_environment env char key double value Description This routine is used to set a double type parameter Arguments sym_environment env INOUT Pointer to the SYMPHONY environment char key IN The name of the parameter to be set double value OUT New value of the corresponding parameter 89 gt sym_set_str_param void sym_set_str_param sym_environment env char key char value Description This routine is used to set a string type parameter Arguments sym_environment env INOUT Pointer to the SYMPHONY environment char key IN The name of the parameter to be set char value OUT New value of the corresponding parameter 90 gt sym_get_int_param int sym_get_int_param sym_environment env char key Description This routine is used to get the value of an integer type parameter Arguments sym_environment env INOUT Pointer to the SYMPHONY environment char key IN The name of the parameter Return values
239. ures vastly improved over CVS the previous hosting software This has required some reorganiza tion and renaming of the header files SYMPHONY is tightly integrated with other COIN OR projects Due to improved procedures for producing stable releases it will now be much easier for us to determine the exact version of SYMPHONY and all other COIN projects you are using when you report a bug SYMPHONY is distributed with all COIN software needed to build a complete solver Previously other COIN software packages had to be downloaded and installed separately Two features have been deprecated and are no longer supported e The native interfaces to OSL and CPLEX are now deprecated and no longer supported These solvers can be called through the COIN OR OSI interface e Column generation functionality has also been officially deprecated For now there are a number of other software packages that offer better functionality than SYMPHONY for im plementing branch and price algorithms For what s new specifically in Version 5 2 3 please check the README file that comes with the distribution 1 3 A Brief History Since the inception of optimization as a recognized field of study in mathematics researchers have been both intrigued and stymied by the difficulty of solving many of the most interesting classes of discrete optimization problems Even combinatorial problems though conceptually easy to model as integer programs have long re
240. uring The first step is to run a configuration script that will allow the compilation process to be customized for your environment to perform this step switch into the root directory of the distribution and type configure This will set up the default configuration files If you want to override the default settings you can either run the configuration script with command line options or else modify the options in the file share config site A complete list of options with brief explanations can be seen both in the file share config site and by typing configure help recursive See Figure 2 1 for a list of options the user may want to set In order to enable or disable an option either modify the file share config site or add the option as an argument to configuration script For instance running configure enable debug will compile the source files with the debugging flag It is possible to use compilers oter than the default which is g For example to perform at automated build of SYMPHONY using the MSVC compiler cl with GNU autotools in the CYGWIN environment configure with configure enable doscompile msvc 2 2 2 3 Building After configuring the code can be built by typing the commands 11 make make install This will first create the required libraries and executables and then will install them By default the library 1ibSym and the executable symphony will be installed to the 1ib and bin dire
241. user_send_cg_data see Section 6 3 1 function in the master module The user has to allocate space for all the data structures including user itself Note that some or all of this may be done in the function user_send cg data if the Tree Manager LP and CG are all compiled together See that function for more information Arguments void user INOUT Pointer to the user defined data structure Return values USER_ERROR Error CG exits USER_SUCCESS The user received the data properly USER_DEFAULT User did not send any data Invoked from cg_initialize at process start 204 gt user_receive_lp_solution_cg int user_receive_lp_solution_cg void user Description This function is invoked only in the case of parallel computation and only if in the user_send_lp_solution function of the LP module the user opted for packing the current LP solution herself Here she must receive the data sent from there Arguments void user IN Pointer to the user defined data structure Invoked from Whenever an LP solution is received Return values USER_ERROR Error The LP solution was not received and will not be pro cessed USER_SUCCESS The user received the LP solution USER DEFAULT The solution was sent by SYMPHONY and will be received automatically Note SYMPHONY automatically unpacks the level index and iteration number correspond ing to the current LP solution within the current search tree node as well as the objective v
242. uts the user unpacks and wants to be added to the problem either because they are of type VIOLATED_SLACK or type CANDIDATE_CUT_NOT_IN_MATRIX will be deleted from the list of slack cuts after this routine returns Therefore the same warning applies here as in the function user_unpack_cuts Wrapper invoked from select_branching_object 184 gt user_select_candidates int user_select_candidates void user double lpetol int cutnun int slacks_in_matrix_num cut_data slacks_in_matrix int slack_cut_num cut_data slack_cuts int varnum var_desc vars double x char status int cand_num branch_obj candidates int action int bc_level Description The purpose of this function is to generate branching candidates Note that action from user_shall_we_branch is passed on to this function but its value can be changed here see notes at the previous function as well as the candidates in candidates and their number in cand_num if there were any Violated cuts found among the slack cuts not in the matrix can be added to the candidate list These violated cuts will be added to the LP relaxation regardless of the value of action The branch obj structure contains fields similar to the cut_data data structure Branching is accomplished by imposing inequalities which divide the current subprob lem while cutting off the corresponding fractional solution Branching on cuts and variables is treated symmetrically and branc
243. val INOUT The user can return the true objective function value of the solution in the case it is feasible i e eliminating the round off error This variable should also be set to the value of the heuristic solution if one is found int branching In Indicates whether the function is being invoked during strong branching from select branching object or after solving an Lp relaxation from fathom_branch double heur_solution OUT Used to return a solution determined heuristically Return values 178 USER_ERROR Error Solution is considered to be not feasible USER_SUCCESS User checked IP feasibility USER DEFAULT SYMPHONY should test the integrality of the solution to de termine if it is feasible Wrapper invoked from select_branching_object after pre solving the LP relaxation of a child corresponding to a candidate and from fathom_branch after solving an LP relaxation 179 gt user_send feasible_solution int user_send_feasible_solution void user double lpetol Description int varnum int indices double values This function is only used for parallel computation The user can send a feasible solution in custom format to the master module if desired However the default routine suffices in most situations The solution is sent using the communication functions described in Section 5 5 1 in whatever logical format the user wants to use The default is to pack the user indices and values of variable
244. would also sacrifice some efficiency A hybrid scheme allowing the user to have both indexed and algorithmic variables variables with user defined representations is planned for a future version of SYMPHONY For efficiency the problem variables can be divided into two sets the base variables and the extra variables The base variables are active in all subproblems whereas the extra variables can be added and removed There is no theoretical difference between base variables and extra variables however designating a well chosen set of base variables can significantly increase efficiency Because they can move in and out of the problem maintaining extra variables requires additional bookkeeping and computation If the user has reason to believe a priori that a variable is good or has a high probability of having a non zero value in some optimal solution to the problem then that variable should be designated as a base variable It is up to the user to designate which variables should be active in the root subproblem Typically when column generation is used only base variables are active Otherwise all variables must be active in the root node 4 3 2 2 Constraints Because the global list of potential constraints also called cuts is not usually known a priori or is extremely large constraints cannot generally be represented simply by a user assigned index Instead each constraint is assigned a global index only after it becomes active
245. xample in a combinatorial optimization problem branching could be accomplished simply by fixing a variable whose current value is fractional to 0 in one branch and 1 in the other The procedure is described more formally in Figure 4 2 Figure 4 3 gives a high level description of the generic branch and cut algorithm In the remainder of the manual we often use the term search tree This term derives from the common representation of the list of subproblems as the nodes of a graph in which each subproblem is connected only to its parent and its children Storing the subproblems in such a form is an important aspect of our global data structures Since the subproblems correspond to the nodes of this graph they are sometimes be referred to as nodes in the search tree or simply as nodes The root node or root of the tree is the node representing the initial subproblem 4 3 Design of SYMPHONY SYMPHONY was designed with two major goals in mind portability and ease of use With respect to ease of use we aimed for a black box design whereby the user would not be required to know anything about the implementation of the library but only about the user interface With respect to portability we aimed not only for it to be possible to use the framework in a wide 37 variety of settings and on a wide variety of hardware but also for it to perform effectively in all these settings Our primary measure of effectiveness was how well the framework wo
246. xed variables should be priced and 5 allowing for methods 50 of simultaneously pricing out large groups of variables Much of this is already implemented in COIN BCP Because pricing is computationally burdensome it currently takes place only either 1 before branching optional or 2 when a node is about to be pruned depending on the phase see the description of the two phase algorithm in Sect 4 4 3 3 To use dynamic column generation the user must supply a subroutine which generates the column corresponding to a particular user index given the list of active constraints in the current relaxation When column generation occurs each column not currently active that has not been previously fixed by reduced cost is either priced out immediately or becomes active in the current relaxation Only a specified number of columns may enter the problem at a time so when that limit is reached column generation ceases For further discussion of column generation see Sect 4 4 3 3 where the two phase algorithm is described Since the matrix is stored in compressed form considerable computation may be needed to add and remove rows and columns Hence rows and columns are only physically removed from the problem when there are sufficiently many to make it worthwhile Otherwise deleted rows and columns remain in the matrix but are simply ignored by the computation Note that because ineffective rows left in the matrix increase the size of
247. y default SYMPHONY displays the indices or column names if specified and values for each nonzero variable in the solution The user may wish to display a custom version of the solution by interpreting the variables Arguments void user IN double lpetol IN int varnum IN int indices IN double values IN double objval IN Return values Pointer to the user defined data structure For sequential computation a pointer to the user s LP data structure is passed in For parallel computation a pointer to the user s Master data structure is passed in The LP zero tolerance used The number of nonzeros in the solution The indices of the nonzeros The values of the nonzeros The objective function value of the solution USER_ERROR Ignored USER_SUCCESS User displayed the solution SYMPHONY should do nothing USER_DEFAULT SYMPHONY should display the solution in default format Post processing If requested SYMPHONY will display a best solution found so far in the default format 166 gt user_send _ feas_sol int user_send_feas_sol void user int feas_sol_size int feas_sol Description This function is useful for debugging purposes It passes a known feasible solution to the tree manager The tree manager then tracks which current subproblem admits this feasible solution and notifies the user when it gets pruned It is useful for finding out why a known optimal solution never gets discovered Usually this
248. y platform and can be configured either for serial com putation or in a wide variety of parallel modes The parallel version can be built for either a fully distributed environment network of workstations or a shared memory environment simply by changing a few configuration options see Chapter 2 To run in a distributed environment the user must have installed the Parallel Virtual Machine PVM available for free from Oak Ridge National Laboratories To run in a shared memory environment the user must have installed an OpenMP compliant compiler gcc 4 2 is currently the only compiler tested and fully supported 1 2 What s New Starting in SYMPHONY 5 0 we introduced a number of new features that give SYMPHONY some unique capabilities These include the ability to solve biobjective integer programs the ability to 1 warms start the solution procedure and the ability to perform basic sensitivity analyses These capabilities have been further developed and enhanced with the introduction of Versions 5 1 and 5 2 Other new features and enhancements are listed below e SYMPHONY has an interactive optimizer that can be used through a command shell In both the sequential and parallel configurations the user can set parameters load and solve instances interactively and display results and statistics For Windows users this means that SYMPHONY can be invoked using the familiar procedure of double clicking on the symphony exe file in Wind
249. y useful functionality because of the fact that we are unable to distribute code linked to libraries licensed under the GNU General Public License GPL There are also a number of other potentially useful configurations such as the parallel version that we do not distribute in binary form Building from source should be easy in most environments and will give you more flexibility See Section 2 2 for more information on additional functionality available when building from source You can obtain the SYMPHONY binary distribution by visiting https www coin or org download binary SYMPHONY The binaries are currently available for Linux and Windows These binaries are built with the following default options e The associated LP solver is the COIN LP solver CLP e Cut generation is done with COIN s Cut Generation Library CGL e All libraries are compiled statically The optimization level for Linux binaries is O2 Only serial executables are included 2 1 1 Installation in Unix like environments e Unpack the distribution with tar xzvf symphony VER XXX bin tgz where XXX indicates the platform and the version of compiler used to build the distribution Switch into the root directory of the unpacked distribution e First test the executable by going to the bin directory and typing symphony F examples sample mps e To test the callable library the distribution includes sample files in examples directory milp c
250. ynamically using the user_create_subproblem callback see user_lp c but this is more complicated and unnecessary here 65 int match_load_problem sym_environment env user_problem prob 1 int i j index n m nz matbeg matind double matval 1b ub obj rhs rngval char sense is_int set up the inital LP data n prob gt numnodes prob gt numnodes 1 2 m 2 prob gt numnodes nz 2 n Allocate the arrays matbeg int malloc n 1 ISIZE matind int malloc nz ISIZE matval double malloc nz DSIZE obj double malloc n DSIZE lb double calloc n DSIZE ub double malloc n DSIZE rhs double malloc m DSIZE sense char malloc m CSIZE rngval double calloc m DSIZE is_int char malloc n CSIZE Fill out the appropriate data structures each column has exactly two entries index 0 for i 0 i lt prob gt numnodes i for j itl j lt prob gt numnodes j prob gt matchi index i The first component of assignment index prob gt match2 index j The second component of assignment index So we can recover the index later prob gt index i j prob gt index j i index obj index prob gt cost il j Cost of assignment i j is_int index TRUE matbeg index 2k index matval 2 index 13 matval 2 index 1 1 matin
251. you should be able to reproduce make sure the parameter no_cut_timeout appears in the parameter file or start SYMPHONY with the a option This will keep the cut generator from timing out a major source of randomness Furthermore run with only one active node allowed at a time set max_active_nodes to 1 This will keep the tree search from becoming random These two steps should allow runs to be reproduced You still have to be 62 careful but this should make things easier 5 6 3 Checking the Validity of Cuts and Tracing the Optimal Path Sometimes the only evidence of a bug is the fact that the optimal solution to a particular problem is never found This is usually caused by either 1 adding an invalid cut or 2 performing an invalid branching There are two options available for discovering such errors The first is for checking the validity of added cuts This checking must of course be done by the user but SYMPHONY can facilitate such checking To do this the user must fill in the function user check validity of cut see Section 6 3 3 THIS function is called every time a cut is passed from the cut generator to the LP and can function as an independent verifier To do this the user must pass through her own data structures a known feasible solution Then for each cut passed into the function the user can check whether the cut is satisfied by the feasible solution If not then there is a problem Of course the problem co
252. zcnt is the number of nonzero coefficients in the cut indices is an array containing the indices of the columns with nonzero entries and values is an array of the corresponding values The right hand side value is passed in through the variable rhs the range is passed in through the variable range and the sense of the inequality is passed through the variable sense Finally the variable send_to_cp indicates to SYMPHONY whether the cut is globally valid and should be sent to the cut pool or whether it is only to be used locally The only output of the user_find_cuts function is the number of cuts gen erated and this value is returned in the last argument For options to generate generic cuts automatically using the COIN Cut Generation Library see the function user generate cuts in 1p06 3 2 2 206 Arguments void user int iter_num int level index objval int varnum indices values double ub double lpetol int cutnum Return values USER_ERROR Ignored Pointer to the user defined data structure The iteration number of the current LP solution The level in the tree on which the current LP solution was generated The index of the node in which LP solution was generated The objective function value of the current LP solution The number of nonzeros in the current LP solution The column indices of the nonzero variables in the current LP solution The values of the nonzero variables listed in indices The cu

SYMPHONY 5.2.3 User`s Manual 1 - Coin-OR

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