Package com.google.ortools.bop

Interface BopParametersOrBuilder

  • All Superinterfaces:
    com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
    All Known Implementing Classes:
    BopParameters, BopParameters.Builder
    public interface BopParametersOrBuilder
extends com.google.protobuf.MessageOrBuilder

    • Method Detail

      • hasMaxTimeInSeconds

        boolean hasMaxTimeInSeconds()
         Maximum time allowed in seconds to solve a problem.
 The counter will starts as soon as Solve() is called.
        optional double max_time_in_seconds = 1 [default = inf];
        Returns:
        Whether the maxTimeInSeconds field is set.

      • getMaxTimeInSeconds

        double getMaxTimeInSeconds()
         Maximum time allowed in seconds to solve a problem.
 The counter will starts as soon as Solve() is called.
        optional double max_time_in_seconds = 1 [default = inf];
        Returns:
        The maxTimeInSeconds.

      • hasMaxDeterministicTime

        boolean hasMaxDeterministicTime()
         Maximum time allowed in deterministic time to solve a problem.
 The deterministic time should be correlated with the real time used by the
 solver, the time unit being roughly the order of magnitude of a second.
 The counter will starts as soon as SetParameters() or SolveWithTimeLimit()
 is called.
        optional double max_deterministic_time = 27 [default = inf];
        Returns:
        Whether the maxDeterministicTime field is set.

      • getMaxDeterministicTime

        double getMaxDeterministicTime()
         Maximum time allowed in deterministic time to solve a problem.
 The deterministic time should be correlated with the real time used by the
 solver, the time unit being roughly the order of magnitude of a second.
 The counter will starts as soon as SetParameters() or SolveWithTimeLimit()
 is called.
        optional double max_deterministic_time = 27 [default = inf];
        Returns:
        The maxDeterministicTime.

      • hasLpMaxDeterministicTime

        boolean hasLpMaxDeterministicTime()
         The max deterministic time given to the LP solver each time it is called.
 If this is not enough to solve the LP at hand, it will simply be called
 again later (and the solve will resume from where it stopped).
        optional double lp_max_deterministic_time = 37 [default = 1];
        Returns:
        Whether the lpMaxDeterministicTime field is set.

      • getLpMaxDeterministicTime

        double getLpMaxDeterministicTime()
         The max deterministic time given to the LP solver each time it is called.
 If this is not enough to solve the LP at hand, it will simply be called
 again later (and the solve will resume from where it stopped).
        optional double lp_max_deterministic_time = 37 [default = 1];
        Returns:
        The lpMaxDeterministicTime.

      • hasMaxNumberOfConsecutiveFailingOptimizerCalls

        boolean hasMaxNumberOfConsecutiveFailingOptimizerCalls()
         Maximum number of consecutive optimizer calls without improving the
 current solution. If this number is reached, the search will be aborted.
 Note that this parameter only applies when an initial solution has been
 found or is provided. Also note that there is no limit to the number of
 calls, when the parameter is not set.
        optional int32 max_number_of_consecutive_failing_optimizer_calls = 35;
        Returns:
        Whether the maxNumberOfConsecutiveFailingOptimizerCalls field is set.

      • getMaxNumberOfConsecutiveFailingOptimizerCalls

        int getMaxNumberOfConsecutiveFailingOptimizerCalls()
         Maximum number of consecutive optimizer calls without improving the
 current solution. If this number is reached, the search will be aborted.
 Note that this parameter only applies when an initial solution has been
 found or is provided. Also note that there is no limit to the number of
 calls, when the parameter is not set.
        optional int32 max_number_of_consecutive_failing_optimizer_calls = 35;
        Returns:
        The maxNumberOfConsecutiveFailingOptimizerCalls.

      • hasRelativeGapLimit

        boolean hasRelativeGapLimit()
         Limit used to stop the optimization as soon as the relative gap is smaller
 than the given value.
 The relative gap is defined as:
   abs(solution_cost - best_bound)
        / max(abs(solution_cost), abs(best_bound)).
        optional double relative_gap_limit = 28 [default = 0.0001];
        Returns:
        Whether the relativeGapLimit field is set.

      • getRelativeGapLimit

        double getRelativeGapLimit()
         Limit used to stop the optimization as soon as the relative gap is smaller
 than the given value.
 The relative gap is defined as:
   abs(solution_cost - best_bound)
        / max(abs(solution_cost), abs(best_bound)).
        optional double relative_gap_limit = 28 [default = 0.0001];
        Returns:
        The relativeGapLimit.

      • hasMaxNumDecisionsInLs

        boolean hasMaxNumDecisionsInLs()
         Maximum number of cascading decisions the solver might use to repair the
 current solution in the LS.
        optional int32 max_num_decisions_in_ls = 2 [default = 4];
        Returns:
        Whether the maxNumDecisionsInLs field is set.

      • getMaxNumDecisionsInLs

        int getMaxNumDecisionsInLs()
         Maximum number of cascading decisions the solver might use to repair the
 current solution in the LS.
        optional int32 max_num_decisions_in_ls = 2 [default = 4];
        Returns:
        The maxNumDecisionsInLs.

      • hasMaxNumBrokenConstraintsInLs

        boolean hasMaxNumBrokenConstraintsInLs()
         Abort the LS search tree as soon as strictly more than this number of
 constraints are broken. The default is a large value which basically
 disable this heuristic.
        optional int32 max_num_broken_constraints_in_ls = 38 [default = 2147483647];
        Returns:
        Whether the maxNumBrokenConstraintsInLs field is set.

      • getMaxNumBrokenConstraintsInLs

        int getMaxNumBrokenConstraintsInLs()
         Abort the LS search tree as soon as strictly more than this number of
 constraints are broken. The default is a large value which basically
 disable this heuristic.
        optional int32 max_num_broken_constraints_in_ls = 38 [default = 2147483647];
        Returns:
        The maxNumBrokenConstraintsInLs.

      • hasLogSearchProgress

        boolean hasLogSearchProgress()
         Whether the solver should log the search progress to LOG(INFO).
        optional bool log_search_progress = 14 [default = false];
        Returns:
        Whether the logSearchProgress field is set.

      • getLogSearchProgress

        boolean getLogSearchProgress()
         Whether the solver should log the search progress to LOG(INFO).
        optional bool log_search_progress = 14 [default = false];
        Returns:
        The logSearchProgress.

      • hasComputeEstimatedImpact

        boolean hasComputeEstimatedImpact()
         Compute estimated impact at each iteration when true; only once when false.
        optional bool compute_estimated_impact = 3 [default = true];
        Returns:
        Whether the computeEstimatedImpact field is set.

      • getComputeEstimatedImpact

        boolean getComputeEstimatedImpact()
         Compute estimated impact at each iteration when true; only once when false.
        optional bool compute_estimated_impact = 3 [default = true];
        Returns:
        The computeEstimatedImpact.

      • hasPruneSearchTree

        boolean hasPruneSearchTree()
         Avoid exploring both branches (b, a, ...) and (a, b, ...).
        optional bool prune_search_tree = 4 [default = false];
        Returns:
        Whether the pruneSearchTree field is set.

      • getPruneSearchTree

        boolean getPruneSearchTree()
         Avoid exploring both branches (b, a, ...) and (a, b, ...).
        optional bool prune_search_tree = 4 [default = false];
        Returns:
        The pruneSearchTree.

      • hasSortConstraintsByNumTerms

        boolean hasSortConstraintsByNumTerms()
         Sort constraints by increasing total number of terms instead of number of
 contributing terms.
        optional bool sort_constraints_by_num_terms = 5 [default = false];
        Returns:
        Whether the sortConstraintsByNumTerms field is set.

      • getSortConstraintsByNumTerms

        boolean getSortConstraintsByNumTerms()
         Sort constraints by increasing total number of terms instead of number of
 contributing terms.
        optional bool sort_constraints_by_num_terms = 5 [default = false];
        Returns:
        The sortConstraintsByNumTerms.

      • hasUseRandomLns

        boolean hasUseRandomLns()
         Use the random Large Neighborhood Search instead of the exhaustive one.
        optional bool use_random_lns = 6 [default = true];
        Returns:
        Whether the useRandomLns field is set.

      • getUseRandomLns

        boolean getUseRandomLns()
         Use the random Large Neighborhood Search instead of the exhaustive one.
        optional bool use_random_lns = 6 [default = true];
        Returns:
        The useRandomLns.

      • hasRandomSeed

        boolean hasRandomSeed()
         The seed used to initialize the random generator.

 TODO(user): Some of our client test fail depending on this value! we need
 to fix them and ideally randomize our behavior from on test to the next so
 that this doesn't happen in the future.
        optional int32 random_seed = 7 [default = 8];
        Returns:
        Whether the randomSeed field is set.

      • getRandomSeed

        int getRandomSeed()
         The seed used to initialize the random generator.

 TODO(user): Some of our client test fail depending on this value! we need
 to fix them and ideally randomize our behavior from on test to the next so
 that this doesn't happen in the future.
        optional int32 random_seed = 7 [default = 8];
        Returns:
        The randomSeed.

      • hasNumRelaxedVars

        boolean hasNumRelaxedVars()
         Number of variables to relax in the exhaustive Large Neighborhood Search.
        optional int32 num_relaxed_vars = 8 [default = 10];
        Returns:
        Whether the numRelaxedVars field is set.

      • getNumRelaxedVars

        int getNumRelaxedVars()
         Number of variables to relax in the exhaustive Large Neighborhood Search.
        optional int32 num_relaxed_vars = 8 [default = 10];
        Returns:
        The numRelaxedVars.

      • hasMaxNumberOfConflictsInRandomLns

        boolean hasMaxNumberOfConflictsInRandomLns()
         The number of conflicts the SAT solver has to solve a random LNS
 subproblem.
        optional int32 max_number_of_conflicts_in_random_lns = 9 [default = 2500];
        Returns:
        Whether the maxNumberOfConflictsInRandomLns field is set.

      • getMaxNumberOfConflictsInRandomLns

        int getMaxNumberOfConflictsInRandomLns()
         The number of conflicts the SAT solver has to solve a random LNS
 subproblem.
        optional int32 max_number_of_conflicts_in_random_lns = 9 [default = 2500];
        Returns:
        The maxNumberOfConflictsInRandomLns.

      • hasNumRandomLnsTries

        boolean hasNumRandomLnsTries()
         Number of tries in the random lns.
        optional int32 num_random_lns_tries = 10 [default = 1];
        Returns:
        Whether the numRandomLnsTries field is set.

      • getNumRandomLnsTries

        int getNumRandomLnsTries()
         Number of tries in the random lns.
        optional int32 num_random_lns_tries = 10 [default = 1];
        Returns:
        The numRandomLnsTries.

      • hasMaxNumberOfBacktracksInLs

        boolean hasMaxNumberOfBacktracksInLs()
         Maximum number of backtracks times the number of variables in Local Search,
 ie. max num backtracks == max_number_of_backtracks_in_ls / num variables.
        optional int64 max_number_of_backtracks_in_ls = 11 [default = 100000000];
        Returns:
        Whether the maxNumberOfBacktracksInLs field is set.

      • getMaxNumberOfBacktracksInLs

        long getMaxNumberOfBacktracksInLs()
         Maximum number of backtracks times the number of variables in Local Search,
 ie. max num backtracks == max_number_of_backtracks_in_ls / num variables.
        optional int64 max_number_of_backtracks_in_ls = 11 [default = 100000000];
        Returns:
        The maxNumberOfBacktracksInLs.

      • hasUseLpLns

        boolean hasUseLpLns()
         Use Large Neighborhood Search based on the LP relaxation.
        optional bool use_lp_lns = 12 [default = true];
        Returns:
        Whether the useLpLns field is set.

      • getUseLpLns

        boolean getUseLpLns()
         Use Large Neighborhood Search based on the LP relaxation.
        optional bool use_lp_lns = 12 [default = true];
        Returns:
        The useLpLns.

      • hasUseSatToChooseLnsNeighbourhood

        boolean hasUseSatToChooseLnsNeighbourhood()
         Whether we use sat propagation to choose the lns neighbourhood.
        optional bool use_sat_to_choose_lns_neighbourhood = 15 [default = true];
        Returns:
        Whether the useSatToChooseLnsNeighbourhood field is set.

      • getUseSatToChooseLnsNeighbourhood

        boolean getUseSatToChooseLnsNeighbourhood()
         Whether we use sat propagation to choose the lns neighbourhood.
        optional bool use_sat_to_choose_lns_neighbourhood = 15 [default = true];
        Returns:
        The useSatToChooseLnsNeighbourhood.

      • hasMaxNumberOfConflictsForQuickCheck

        boolean hasMaxNumberOfConflictsForQuickCheck()
         The number of conflicts the SAT solver has to solve a random LNS
 subproblem for the quick check of infeasibility.
        optional int32 max_number_of_conflicts_for_quick_check = 16 [default = 10];
        Returns:
        Whether the maxNumberOfConflictsForQuickCheck field is set.

      • getMaxNumberOfConflictsForQuickCheck

        int getMaxNumberOfConflictsForQuickCheck()
         The number of conflicts the SAT solver has to solve a random LNS
 subproblem for the quick check of infeasibility.
        optional int32 max_number_of_conflicts_for_quick_check = 16 [default = 10];
        Returns:
        The maxNumberOfConflictsForQuickCheck.

      • hasUseSymmetry

        boolean hasUseSymmetry()
         If true, find and exploit the eventual symmetries of the problem.

 TODO(user): turn this on by default once the symmetry finder becomes fast
 enough to be negligeable for most problem. Or at least support a time
 limit.
        optional bool use_symmetry = 17 [default = false];
        Returns:
        Whether the useSymmetry field is set.

      • getUseSymmetry

        boolean getUseSymmetry()
         If true, find and exploit the eventual symmetries of the problem.

 TODO(user): turn this on by default once the symmetry finder becomes fast
 enough to be negligeable for most problem. Or at least support a time
 limit.
        optional bool use_symmetry = 17 [default = false];
        Returns:
        The useSymmetry.

      • hasExploitSymmetryInSatFirstSolution

        boolean hasExploitSymmetryInSatFirstSolution()
         If true, find and exploit symmetries in proving satisfiability in the first
 problem.
 This feature is experimental. On some problems, computing symmetries may
 run forever. You may also run into unforseen problems as this feature was
 not extensively tested.
        optional bool exploit_symmetry_in_sat_first_solution = 40 [default = false];
        Returns:
        Whether the exploitSymmetryInSatFirstSolution field is set.

      • getExploitSymmetryInSatFirstSolution

        boolean getExploitSymmetryInSatFirstSolution()
         If true, find and exploit symmetries in proving satisfiability in the first
 problem.
 This feature is experimental. On some problems, computing symmetries may
 run forever. You may also run into unforseen problems as this feature was
 not extensively tested.
        optional bool exploit_symmetry_in_sat_first_solution = 40 [default = false];
        Returns:
        The exploitSymmetryInSatFirstSolution.

      • hasMaxNumberOfConflictsInRandomSolutionGeneration

        boolean hasMaxNumberOfConflictsInRandomSolutionGeneration()
         The number of conflicts the SAT solver has to generate a random solution.
        optional int32 max_number_of_conflicts_in_random_solution_generation = 20 [default = 500];
        Returns:
        Whether the maxNumberOfConflictsInRandomSolutionGeneration field is set.

      • getMaxNumberOfConflictsInRandomSolutionGeneration

        int getMaxNumberOfConflictsInRandomSolutionGeneration()
         The number of conflicts the SAT solver has to generate a random solution.
        optional int32 max_number_of_conflicts_in_random_solution_generation = 20 [default = 500];
        Returns:
        The maxNumberOfConflictsInRandomSolutionGeneration.

      • hasMaxNumberOfExploredAssignmentsPerTryInLs

        boolean hasMaxNumberOfExploredAssignmentsPerTryInLs()
         The maximum number of assignments the Local Search iterates on during one
 try. Note that if the Local Search is called again on the same solution
 it will not restart from scratch but will iterate on the next
 max_number_of_explored_assignments_per_try_in_ls assignments.
        optional int64 max_number_of_explored_assignments_per_try_in_ls = 21 [default = 10000];
        Returns:
        Whether the maxNumberOfExploredAssignmentsPerTryInLs field is set.

      • getMaxNumberOfExploredAssignmentsPerTryInLs

        long getMaxNumberOfExploredAssignmentsPerTryInLs()
         The maximum number of assignments the Local Search iterates on during one
 try. Note that if the Local Search is called again on the same solution
 it will not restart from scratch but will iterate on the next
 max_number_of_explored_assignments_per_try_in_ls assignments.
        optional int64 max_number_of_explored_assignments_per_try_in_ls = 21 [default = 10000];
        Returns:
        The maxNumberOfExploredAssignmentsPerTryInLs.

      • hasUseTranspositionTableInLs

        boolean hasUseTranspositionTableInLs()
         Whether we use an hash set during the LS to avoid exploring more than once
 the "same" state. Note that because the underlying SAT solver may learn
 information in the middle of the LS, this may make the LS slightly less
 "complete", but it should be faster.
        optional bool use_transposition_table_in_ls = 22 [default = true];
        Returns:
        Whether the useTranspositionTableInLs field is set.

      • getUseTranspositionTableInLs

        boolean getUseTranspositionTableInLs()
         Whether we use an hash set during the LS to avoid exploring more than once
 the "same" state. Note that because the underlying SAT solver may learn
 information in the middle of the LS, this may make the LS slightly less
 "complete", but it should be faster.
        optional bool use_transposition_table_in_ls = 22 [default = true];
        Returns:
        The useTranspositionTableInLs.

      • hasUsePotentialOneFlipRepairsInLs

        boolean hasUsePotentialOneFlipRepairsInLs()
         Whether we keep a list of variable that can potentially repair in one flip
 all the current infeasible constraints (such variable must at least appear
 in all the infeasible constraints for this to happen).
        optional bool use_potential_one_flip_repairs_in_ls = 39 [default = false];
        Returns:
        Whether the usePotentialOneFlipRepairsInLs field is set.

      • getUsePotentialOneFlipRepairsInLs

        boolean getUsePotentialOneFlipRepairsInLs()
         Whether we keep a list of variable that can potentially repair in one flip
 all the current infeasible constraints (such variable must at least appear
 in all the infeasible constraints for this to happen).
        optional bool use_potential_one_flip_repairs_in_ls = 39 [default = false];
        Returns:
        The usePotentialOneFlipRepairsInLs.

      • hasUseLearnedBinaryClausesInLp

        boolean hasUseLearnedBinaryClausesInLp()
         Whether we use the learned binary clauses in the Linear Relaxation.
        optional bool use_learned_binary_clauses_in_lp = 23 [default = true];
        Returns:
        Whether the useLearnedBinaryClausesInLp field is set.

      • getUseLearnedBinaryClausesInLp

        boolean getUseLearnedBinaryClausesInLp()
         Whether we use the learned binary clauses in the Linear Relaxation.
        optional bool use_learned_binary_clauses_in_lp = 23 [default = true];
        Returns:
        The useLearnedBinaryClausesInLp.

      • hasNumberOfSolvers

        boolean hasNumberOfSolvers()
         The number of solvers used to run Bop. Note that one thread will be created
 per solver. The type of communication between solvers is specified by the
 synchronization_type parameter.
        optional int32 number_of_solvers = 24 [default = 1];
        Returns:
        Whether the numberOfSolvers field is set.

      • getNumberOfSolvers

        int getNumberOfSolvers()
         The number of solvers used to run Bop. Note that one thread will be created
 per solver. The type of communication between solvers is specified by the
 synchronization_type parameter.
        optional int32 number_of_solvers = 24 [default = 1];
        Returns:
        The numberOfSolvers.

      • hasSynchronizationType

        boolean hasSynchronizationType()
        optional .operations_research.bop.BopParameters.ThreadSynchronizationType synchronization_type = 25 [default = NO_SYNCHRONIZATION];
        Returns:
        Whether the synchronizationType field is set.

      • getSynchronizationType

        BopParameters.ThreadSynchronizationType getSynchronizationType()
        optional .operations_research.bop.BopParameters.ThreadSynchronizationType synchronization_type = 25 [default = NO_SYNCHRONIZATION];
        Returns:
        The synchronizationType.

      • getSolverOptimizerSetsList

        java.util.List<BopSolverOptimizerSet> getSolverOptimizerSetsList()
         List of set of optimizers to be run by the solvers.
 Note that the i_th solver will run the
 min(i, solver_optimizer_sets_size())_th optimizer set.
 The default is defined by default_solver_optimizer_sets (only one set).
        repeated .operations_research.bop.BopSolverOptimizerSet solver_optimizer_sets = 26;

      • getSolverOptimizerSets

        BopSolverOptimizerSet getSolverOptimizerSets​(int index)
         List of set of optimizers to be run by the solvers.
 Note that the i_th solver will run the
 min(i, solver_optimizer_sets_size())_th optimizer set.
 The default is defined by default_solver_optimizer_sets (only one set).
        repeated .operations_research.bop.BopSolverOptimizerSet solver_optimizer_sets = 26;

      • getSolverOptimizerSetsCount

        int getSolverOptimizerSetsCount()
         List of set of optimizers to be run by the solvers.
 Note that the i_th solver will run the
 min(i, solver_optimizer_sets_size())_th optimizer set.
 The default is defined by default_solver_optimizer_sets (only one set).
        repeated .operations_research.bop.BopSolverOptimizerSet solver_optimizer_sets = 26;

      • getSolverOptimizerSetsOrBuilderList

        java.util.List<? extends BopSolverOptimizerSetOrBuilder> getSolverOptimizerSetsOrBuilderList()
         List of set of optimizers to be run by the solvers.
 Note that the i_th solver will run the
 min(i, solver_optimizer_sets_size())_th optimizer set.
 The default is defined by default_solver_optimizer_sets (only one set).
        repeated .operations_research.bop.BopSolverOptimizerSet solver_optimizer_sets = 26;

      • getSolverOptimizerSetsOrBuilder

        BopSolverOptimizerSetOrBuilder getSolverOptimizerSetsOrBuilder​(int index)
         List of set of optimizers to be run by the solvers.
 Note that the i_th solver will run the
 min(i, solver_optimizer_sets_size())_th optimizer set.
 The default is defined by default_solver_optimizer_sets (only one set).
        repeated .operations_research.bop.BopSolverOptimizerSet solver_optimizer_sets = 26;

      • hasDefaultSolverOptimizerSets

        boolean hasDefaultSolverOptimizerSets()
        optional string default_solver_optimizer_sets = 33 [default = "methods:{type:LOCAL_SEARCH } methods:{type:RANDOM_FIRST_SOLUTION } methods:{type:LINEAR_RELAXATION } methods:{type:LP_FIRST_SOLUTION } methods:{type:OBJECTIVE_FIRST_SOLUTION } methods:{type:USER_GUIDED_FIRST_SOLUTION } methods:{type:RANDOM_CONSTRAINT_LNS_GUIDED_BY_LP } methods:{type:RANDOM_VARIABLE_LNS_GUIDED_BY_LP } methods:{type:RELATION_GRAPH_LNS } methods:{type:RELATION_GRAPH_LNS_GUIDED_BY_LP } methods:{type:RANDOM_CONSTRAINT_LNS } methods:{type:RANDOM_VARIABLE_LNS } methods:{type:SAT_CORE_BASED } methods:{type:COMPLETE_LNS } "];
        Returns:
        Whether the defaultSolverOptimizerSets field is set.

      • getDefaultSolverOptimizerSets

        java.lang.String getDefaultSolverOptimizerSets()
        optional string default_solver_optimizer_sets = 33 [default = "methods:{type:LOCAL_SEARCH } methods:{type:RANDOM_FIRST_SOLUTION } methods:{type:LINEAR_RELAXATION } methods:{type:LP_FIRST_SOLUTION } methods:{type:OBJECTIVE_FIRST_SOLUTION } methods:{type:USER_GUIDED_FIRST_SOLUTION } methods:{type:RANDOM_CONSTRAINT_LNS_GUIDED_BY_LP } methods:{type:RANDOM_VARIABLE_LNS_GUIDED_BY_LP } methods:{type:RELATION_GRAPH_LNS } methods:{type:RELATION_GRAPH_LNS_GUIDED_BY_LP } methods:{type:RANDOM_CONSTRAINT_LNS } methods:{type:RANDOM_VARIABLE_LNS } methods:{type:SAT_CORE_BASED } methods:{type:COMPLETE_LNS } "];
        Returns:
        The defaultSolverOptimizerSets.

      • getDefaultSolverOptimizerSetsBytes

        com.google.protobuf.ByteString getDefaultSolverOptimizerSetsBytes()
        optional string default_solver_optimizer_sets = 33 [default = "methods:{type:LOCAL_SEARCH } methods:{type:RANDOM_FIRST_SOLUTION } methods:{type:LINEAR_RELAXATION } methods:{type:LP_FIRST_SOLUTION } methods:{type:OBJECTIVE_FIRST_SOLUTION } methods:{type:USER_GUIDED_FIRST_SOLUTION } methods:{type:RANDOM_CONSTRAINT_LNS_GUIDED_BY_LP } methods:{type:RANDOM_VARIABLE_LNS_GUIDED_BY_LP } methods:{type:RELATION_GRAPH_LNS } methods:{type:RELATION_GRAPH_LNS_GUIDED_BY_LP } methods:{type:RANDOM_CONSTRAINT_LNS } methods:{type:RANDOM_VARIABLE_LNS } methods:{type:SAT_CORE_BASED } methods:{type:COMPLETE_LNS } "];
        Returns:
        The bytes for defaultSolverOptimizerSets.

      • hasUseLpStrongBranching

        boolean hasUseLpStrongBranching()
         Use strong branching in the linear relaxation optimizer.
 The strong branching is a what-if analysis on each variable v, i.e.
 compute the best bound when v is assigned to true, compute the best bound
 when v is assigned to false, and then use those best bounds to improve the
 overall best bound.
 This is useful to improve the best_bound, but also to fix some variables
 during search.
 Note that using probing might be time consuming as it runs the LP solver
 2 * num_variables times.
        optional bool use_lp_strong_branching = 29 [default = false];
        Returns:
        Whether the useLpStrongBranching field is set.

      • getUseLpStrongBranching

        boolean getUseLpStrongBranching()
         Use strong branching in the linear relaxation optimizer.
 The strong branching is a what-if analysis on each variable v, i.e.
 compute the best bound when v is assigned to true, compute the best bound
 when v is assigned to false, and then use those best bounds to improve the
 overall best bound.
 This is useful to improve the best_bound, but also to fix some variables
 during search.
 Note that using probing might be time consuming as it runs the LP solver
 2 * num_variables times.
        optional bool use_lp_strong_branching = 29 [default = false];
        Returns:
        The useLpStrongBranching.

      • hasDecomposerNumVariablesThreshold

        boolean hasDecomposerNumVariablesThreshold()
         Only try to decompose the problem when the number of variables is greater
 than the threshold.
        optional int32 decomposer_num_variables_threshold = 30 [default = 50];
        Returns:
        Whether the decomposerNumVariablesThreshold field is set.

      • getDecomposerNumVariablesThreshold

        int getDecomposerNumVariablesThreshold()
         Only try to decompose the problem when the number of variables is greater
 than the threshold.
        optional int32 decomposer_num_variables_threshold = 30 [default = 50];
        Returns:
        The decomposerNumVariablesThreshold.

      • hasNumBopSolversUsedByDecomposition

        boolean hasNumBopSolversUsedByDecomposition()
         The number of BopSolver created (thread pool workers) used by the integral
 solver to solve a decomposed problem.
 TODO(user): Merge this with the number_of_solvers parameter.
        optional int32 num_bop_solvers_used_by_decomposition = 31 [default = 1];
        Returns:
        Whether the numBopSolversUsedByDecomposition field is set.

      • getNumBopSolversUsedByDecomposition

        int getNumBopSolversUsedByDecomposition()
         The number of BopSolver created (thread pool workers) used by the integral
 solver to solve a decomposed problem.
 TODO(user): Merge this with the number_of_solvers parameter.
        optional int32 num_bop_solvers_used_by_decomposition = 31 [default = 1];
        Returns:
        The numBopSolversUsedByDecomposition.

      • hasDecomposedProblemMinTimeInSeconds

        boolean hasDecomposedProblemMinTimeInSeconds()
         HACK. To avoid spending too little time on small problems, spend at least
 this time solving each of the decomposed sub-problem. This only make sense
 if num_bop_solvers_used_by_decomposition is greater than 1 so that the
 overhead can be "absorbed" by the other threads.
        optional double decomposed_problem_min_time_in_seconds = 36 [default = 0];
        Returns:
        Whether the decomposedProblemMinTimeInSeconds field is set.

      • getDecomposedProblemMinTimeInSeconds

        double getDecomposedProblemMinTimeInSeconds()
         HACK. To avoid spending too little time on small problems, spend at least
 this time solving each of the decomposed sub-problem. This only make sense
 if num_bop_solvers_used_by_decomposition is greater than 1 so that the
 overhead can be "absorbed" by the other threads.
        optional double decomposed_problem_min_time_in_seconds = 36 [default = 0];
        Returns:
        The decomposedProblemMinTimeInSeconds.

      • hasGuidedSatConflictsChunk

        boolean hasGuidedSatConflictsChunk()
         The first solutions based on guided SAT will work in chunk of that many
 conflicts at the time. This allows to simulate parallelism between the
 different guiding strategy on a single core.
        optional int32 guided_sat_conflicts_chunk = 34 [default = 1000];
        Returns:
        Whether the guidedSatConflictsChunk field is set.

      • getGuidedSatConflictsChunk

        int getGuidedSatConflictsChunk()
         The first solutions based on guided SAT will work in chunk of that many
 conflicts at the time. This allows to simulate parallelism between the
 different guiding strategy on a single core.
        optional int32 guided_sat_conflicts_chunk = 34 [default = 1000];
        Returns:
        The guidedSatConflictsChunk.

      • hasMaxLpSolveForFeasibilityProblems

        boolean hasMaxLpSolveForFeasibilityProblems()
         The maximum number of time the LP solver will run to feasibility for pure
 feasibility problems (with a constant-valued objective function). Set this
 to a small value, e.g., 1, if fractional solutions offer useful guidance to
 other solvers in the portfolio. A negative value means no limit.
        optional int32 max_lp_solve_for_feasibility_problems = 41 [default = 0];
        Returns:
        Whether the maxLpSolveForFeasibilityProblems field is set.

      • getMaxLpSolveForFeasibilityProblems

        int getMaxLpSolveForFeasibilityProblems()
         The maximum number of time the LP solver will run to feasibility for pure
 feasibility problems (with a constant-valued objective function). Set this
 to a small value, e.g., 1, if fractional solutions offer useful guidance to
 other solvers in the portfolio. A negative value means no limit.
        optional int32 max_lp_solve_for_feasibility_problems = 41 [default = 0];
        Returns:
        The maxLpSolveForFeasibilityProblems.