All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

SatParameters, SatParameters.Builder
```
public interface SatParametersOrBuilder
extends com.google.protobuf.MessageOrBuilder
```

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`double`	`getAbsoluteGapLimit()`	Stop the search when the gap between the best feasible objective (O) and our best objective bound (B) is smaller than a limit.
`boolean`	`getAddCgCuts()`	Whether we generate and add Chvatal-Gomory cuts to the LP at root node.
`boolean`	`getAddCliqueCuts()`	Whether we generate clique cuts from the binary implication graph.
`boolean`	`getAddLinMaxCuts()`	For the lin max constraints, generates the cuts described in "Strong mixed-integer programming formulations for trained neural networks" by Ross Anderson et.
`boolean`	`getAddLpConstraintsLazily()`	If true, we start by an empty LP, and only add constraints not satisfied by the current LP solution batch by batch.
`boolean`	`getAddMirCuts()`	Whether we generate MIR cuts at root node.
`boolean`	`getAddObjectiveCut()`	When the LP objective is fractional, do we add the cut that forces the linear objective expression to be greater or equal to this fractional value rounded up? We can always do that since our objective is integer, and combined with MIR heuristic to reduce the coefficient of such cut, it can help.
`boolean`	`getAddZeroHalfCuts()`	Whether we generate Zero-Half cuts at root node.
`boolean`	`getAlsoBumpVariablesInConflictReasons()`	When this is true, then the variables that appear in any of the reason of the variables in a conflict have their activity bumped.
`boolean`	`getAutoDetectGreaterThanAtLeastOneOf()`	If true, then the precedences propagator try to detect for each variable if it has a set of "optional incoming arc" for which at least one of them is present.
`SatParameters.BinaryMinizationAlgorithm`	`getBinaryMinimizationAlgorithm()`	`optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FIRST];`
`int`	`getBinarySearchNumConflicts()`	If non-negative, perform a binary search on the objective variable in order to find an [min, max] interval outside of which the solver proved unsat/sat under this amount of conflict.
`double`	`getBlockingRestartMultiplier()`	`optional double blocking_restart_multiplier = 66 [default = 1.4];`
`int`	`getBlockingRestartWindowSize()`	`optional int32 blocking_restart_window_size = 65 [default = 5000];`
`int`	`getBooleanEncodingLevel()`	A non-negative level indicating how much we should try to fully encode Integer variables as Boolean.
`boolean`	`getCatchSigintSignal()`	Indicates if the CP-SAT layer should catch Control-C (SIGINT) signals when calling solve.
`double`	`getClauseActivityDecay()`	Clause activity parameters (same effect as the one on the variables).
`int`	`getClauseCleanupLbdBound()`	All the clauses with a LBD (literal blocks distance) lower or equal to this parameters will always be kept.
`SatParameters.ClauseOrdering`	`getClauseCleanupOrdering()`	`optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];`
`int`	`getClauseCleanupPeriod()`	Trigger a cleanup when this number of "deletable" clauses is learned.
`SatParameters.ClauseProtection`	`getClauseCleanupProtection()`	`optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE];`
`double`	`getClauseCleanupRatio()`	During a cleanup, if clause_cleanup_target is 0, we will delete the clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed target of clauses to keep.
`int`	`getClauseCleanupTarget()`	During a cleanup, we will always keep that number of "deletable" clauses.
`boolean`	`getConvertIntervals()`	Temporary flag util the feature is more mature.
`int`	`getCoreMinimizationLevel()`	If positive, we spend some effort on each core: - At level 1, we use a simple heuristic to try to minimize an UNSAT core.
`boolean`	`getCountAssumptionLevelsInLbd()`	Whether or not the assumption levels are taken into account during the LBD computation.
`boolean`	`getCoverOptimization()`	If true, when the max-sat algo find a core, we compute the minimal number of literals in the core that needs to be true to have a feasible solution.
`boolean`	`getCpModelPresolve()`	Whether we presolve the cp_model before solving it.
`int`	`getCpModelProbingLevel()`	How much effort do we spend on probing.
`boolean`	`getCpModelUseSatPresolve()`	Whether we also use the sat presolve when cp_model_presolve is true.
`double`	`getCutActiveCountDecay()`	`optional double cut_active_count_decay = 156 [default = 0.8];`
`int`	`getCutCleanupTarget()`	Target number of constraints to remove during cleanup.
`int`	`getCutLevel()`	Control the global cut effort.
`double`	`getCutMaxActiveCountValue()`	These parameters are similar to sat clause management activity parameters.
`boolean`	`getDebugCrashOnBadHint()`	Crash if we do not manage to complete the hint into a full solution.
`int`	`getDebugMaxNumPresolveOperations()`	If positive, try to stop just after that many presolve rules have been applied.
`boolean`	`getDebugPostsolveWithFullSolver()`	We have two different postsolve code.
`java.lang.String`	`getDefaultRestartAlgorithms()`	`optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];`
`com.google.protobuf.ByteString`	`getDefaultRestartAlgorithmsBytes()`	`optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];`
`boolean`	`getDetectTableWithCost()`	If true, we detect variable that are unique to a table constraint and only there to encode a cost on each tuple.
`boolean`	`getDisableConstraintExpansion()`	If true, it disable all constraint expansion.
`boolean`	`getDiversifyLnsParams()`	If true, registers more lns subsolvers with different parameters.
`boolean`	`getEncodeComplexLinearConstraintWithInteger()`	Linear constraint with a complex right hand side (more than a single interval) need to be expanded, there is a couple of way to do that.
`boolean`	`getEnumerateAllSolutions()`	Whether we enumerate all solutions of a problem without objective.
`boolean`	`getExpandAlldiffConstraints()`	If true, expand all_different constraints that are not permutations.
`boolean`	`getExpandReservoirConstraints()`	If true, expand the reservoir constraints by creating booleans for all possible precedences between event and encoding the constraint.
`boolean`	`getExploitAllLpSolution()`	If true and the Lp relaxation of the problem has a solution, try to exploit it.
`boolean`	`getExploitAllPrecedences()`	`optional bool exploit_all_precedences = 220 [default = false];`
`boolean`	`getExploitBestSolution()`	When branching on a variable, follow the last best solution value.
`boolean`	`getExploitIntegerLpSolution()`	If true and the Lp relaxation of the problem has an integer optimal solution, try to exploit it.
`boolean`	`getExploitObjective()`	When branching an a variable that directly affect the objective, branch on the value that lead to the best objective first.
`boolean`	`getExploitRelaxationSolution()`	When branching on a variable, follow the last best relaxation solution value.
`java.lang.String`	`getExtraSubsolvers(int index)`	A convenient way to add more workers types.
`com.google.protobuf.ByteString`	`getExtraSubsolversBytes(int index)`	A convenient way to add more workers types.
`int`	`getExtraSubsolversCount()`	A convenient way to add more workers types.
`java.util.List<java.lang.String>`	`getExtraSubsolversList()`	A convenient way to add more workers types.
`double`	`getFeasibilityJumpDecay()`	On each restart, we randomly choose if we use decay (with this parameter) or no decay.
`boolean`	`getFeasibilityJumpEnableRestarts()`	When stagnating, feasibility jump will either restart from a default solution (with some possible randomization), or randomly pertubate the current solution.
`int`	`getFeasibilityJumpLinearizationLevel()`	How much do we linearize the problem in the local search code.
`int`	`getFeasibilityJumpMaxExpandedConstraintSize()`	Maximum size of no_overlap or no_overlap_2d constraint for a quadratic expansion.
`int`	`getFeasibilityJumpRestartFactor()`	This is a factor that directly influence the work before each restart.
`double`	`getFeasibilityJumpVarPerburbationRangeRatio()`	Max distance between the default value and the pertubated value relative to the range of the domain of the variable.
`double`	`getFeasibilityJumpVarRandomizationProbability()`	Probability for a variable to have a non default value upon restarts or perturbations.
`boolean`	`getFillAdditionalSolutionsInResponse()`	If true, the final response addition_solutions field will be filled with all solutions from our solutions pool.
`boolean`	`getFillTightenedDomainsInResponse()`	If true, add information about the derived variable domains to the CpSolverResponse.
`boolean`	`getFindBigLinearOverlap()`	Try to find large "rectangle" in the linear constraint matrix with identical lines.
`boolean`	`getFindMultipleCores()`	Whether we try to find more independent cores for a given set of assumptions in the core based max-SAT algorithms.
`boolean`	`getFixVariablesToTheirHintedValue()`	If true, variables appearing in the solution hints will be fixed to their hinted value.
`SatParameters.FPRoundingMethod`	`getFpRounding()`	`optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];`
`double`	`getGlucoseDecayIncrement()`	`optional double glucose_decay_increment = 23 [default = 0.01];`
`int`	`getGlucoseDecayIncrementPeriod()`	`optional int32 glucose_decay_increment_period = 24 [default = 5000];`
`double`	`getGlucoseMaxDecay()`	The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until 0.95.
`int`	`getHintConflictLimit()`	Conflict limit used in the phase that exploit the solution hint.
`boolean`	`getIgnoreNames()`	If true, we don't keep names in our internal copy of the user given model.
`java.lang.String`	`getIgnoreSubsolvers(int index)`	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem.
`com.google.protobuf.ByteString`	`getIgnoreSubsolversBytes(int index)`	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem.
`int`	`getIgnoreSubsolversCount()`	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem.
`java.util.List<java.lang.String>`	`getIgnoreSubsolversList()`	Rather than fully specifying subsolvers, it is often convenient to just remove the ones that are not useful on a given problem.
`boolean`	`getInferAllDiffs()`	Run a max-clique code amongst all the x != y we can find and try to infer set of variables that are all different.
`SatParameters.Polarity`	`getInitialPolarity()`	`optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];`
`double`	`getInitialVariablesActivity()`	The initial value of the variables activity.
`boolean`	`getInstantiateAllVariables()`	If true, the solver will add a default integer branching strategy to the already defined search strategy.
`int`	`getInterleaveBatchSize()`	`optional int32 interleave_batch_size = 134 [default = 0];`
`boolean`	`getInterleaveSearch()`	Experimental.
`boolean`	`getKeepAllFeasibleSolutionsInPresolve()`	If true, we disable the presolve reductions that remove feasible solutions from the search space.
`int`	`getLinearizationLevel()`	A non-negative level indicating the type of constraints we consider in the LP relaxation.
`int`	`getLinearSplitSize()`	Linear constraints that are not pseudo-Boolean and that are longer than this size will be split into sqrt(size) intermediate sums in order to have faster propation in the CP engine.
`java.lang.String`	`getLogPrefix()`	Add a prefix to all logs.
`com.google.protobuf.ByteString`	`getLogPrefixBytes()`	Add a prefix to all logs.
`boolean`	`getLogSearchProgress()`	Whether the solver should log the search progress.
`boolean`	`getLogSubsolverStatistics()`	Whether the solver should display per sub-solver search statistics.
`boolean`	`getLogToResponse()`	Log to response proto.
`boolean`	`getLogToStdout()`	Log to stdout.
`double`	`getLpDualTolerance()`	`optional double lp_dual_tolerance = 267 [default = 1e-07];`
`double`	`getLpPrimalTolerance()`	The internal LP tolerances used by CP-SAT.
`int`	`getMaxAllDiffCutSize()`	Cut generator for all diffs can add too many cuts for large all_diff constraints.
`double`	`getMaxClauseActivityValue()`	`optional double max_clause_activity_value = 18 [default = 1e+20];`
`int`	`getMaxConsecutiveInactiveCount()`	If a constraint/cut in LP is not active for that many consecutive OPTIMAL solves, remove it from the LP.
`int`	`getMaxCutRoundsAtLevelZero()`	Max number of time we perform cut generation and resolve the LP at level 0.
`double`	`getMaxDeterministicTime()`	Maximum time allowed in deterministic time to solve a problem.
`int`	`getMaxDomainSizeWhenEncodingEqNeqConstraints()`	When loading ax + by ==/!= c when x and y are both fully encoded.
`int`	`getMaxIntegerRoundingScaling()`	In the integer rounding procedure used for MIR and Gomory cut, the maximum "scaling" we use (must be positive).
`long`	`getMaxMemoryInMb()`	Maximum memory allowed for the whole thread containing the solver.
`long`	`getMaxNumberOfConflicts()`	Maximum number of conflicts allowed to solve a problem.
`int`	`getMaxNumCuts()`	The limit on the number of cuts in our cut pool.
`int`	`getMaxNumIntervalsForTimetableEdgeFinding()`	Max number of intervals for the timetable_edge_finding algorithm to propagate.
`int`	`getMaxPresolveIterations()`	In case of large reduction in a presolve iteration, we perform multiple presolve iterations.
`SatParameters.MaxSatAssumptionOrder`	`getMaxSatAssumptionOrder()`	`optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];`
`boolean`	`getMaxSatReverseAssumptionOrder()`	If true, adds the assumption in the reverse order of the one defined by max_sat_assumption_order.
`SatParameters.MaxSatStratificationAlgorithm`	`getMaxSatStratification()`	`optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];`
`int`	`getMaxSizeToCreatePrecedenceLiteralsInDisjunctive()`	Create one literal for each disjunction of two pairs of tasks.
`double`	`getMaxTimeInSeconds()`	Maximum time allowed in seconds to solve a problem.
`double`	`getMaxVariableActivityValue()`	`optional double max_variable_activity_value = 16 [default = 1e+100];`
`double`	`getMergeAtMostOneWorkLimit()`	`optional double merge_at_most_one_work_limit = 146 [default = 100000000];`
`double`	`getMergeNoOverlapWorkLimit()`	During presolve, we use a maximum clique heuristic to merge together no-overlap constraints or at most one constraints.
`SatParameters.ConflictMinimizationAlgorithm`	`getMinimizationAlgorithm()`	`optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];`
`boolean`	`getMinimizeReductionDuringPbResolution()`	A different algorithm during PB resolution.
`int`	`getMinimizeWithPropagationNumDecisions()`	`optional int32 minimize_with_propagation_num_decisions = 97 [default = 1000];`
`int`	`getMinimizeWithPropagationRestartPeriod()`	Parameters for an heuristic similar to the one described in "An effective learnt clause minimization approach for CDCL Sat Solvers", https://www.ijcai.org/proceedings/2017/0098.pdf For now, we have a somewhat simpler implementation where every x restart we spend y decisions on clause minimization.
`int`	`getMinNumLnsWorkers()`	Obsolete parameter.
`double`	`getMinOrthogonalityForLpConstraints()`	While adding constraints, skip the constraints which have orthogonality less than 'min_orthogonality_for_lp_constraints' with already added constraints during current call.
`boolean`	`getMipAutomaticallyScaleVariables()`	If true, some continuous variable might be automatically scaled.
`double`	`getMipCheckPrecision()`	As explained in mip_precision and mip_max_activity_exponent, we cannot always reach the wanted precision during scaling.
`boolean`	`getMipComputeTrueObjectiveBound()`	Even if we make big error when scaling the objective, we can always derive a correct lower bound on the original objective by using the exact lower bound on the scaled integer version of the objective.
`double`	`getMipDropTolerance()`	Any value in the input mip with a magnitude lower than this will be set to zero.
`int`	`getMipMaxActivityExponent()`	To avoid integer overflow, we always force the maximum possible constraint activity (and objective value) according to the initial variable domain to be smaller than 2 to this given power.
`double`	`getMipMaxBound()`	We need to bound the maximum magnitude of the variables for CP-SAT, and that is the bound we use.
`double`	`getMipMaxValidMagnitude()`	Any finite values in the input MIP must be below this threshold, otherwise the model will be reported invalid.
`int`	`getMipPresolveLevel()`	When solving a MIP, we do some basic floating point presolving before scaling the problem to integer to be handled by CP-SAT.
`boolean`	`getMipScaleLargeDomain()`	If this is false, then mip_var_scaling is only applied to variables with "small" domain.
`double`	`getMipVarScaling()`	All continuous variable of the problem will be multiplied by this factor.
`double`	`getMipWantedPrecision()`	When scaling constraint with double coefficients to integer coefficients, we will multiply by a power of 2 and round the coefficients.
`java.lang.String`	`getName()`	In some context, like in a portfolio of search, it makes sense to name a given parameters set for logging purpose.
`com.google.protobuf.ByteString`	`getNameBytes()`	In some context, like in a portfolio of search, it makes sense to name a given parameters set for logging purpose.
`int`	`getNewConstraintsBatchSize()`	Add that many lazy constraints (or cuts) at once in the LP.
`boolean`	`getNewLinearPropagation()`	Experimental.
`int`	`getNumConflictsBeforeStrategyChanges()`	After each restart, if the number of conflict since the last strategy change is greater that this, then we increment a "strategy_counter" that can be use to change the search strategy used by the following restarts.
`int`	`getNumSearchWorkers()`	`optional int32 num_search_workers = 100 [default = 0];`
`int`	`getNumViolationLs()`	This will create incomplete subsolvers (that are not LNS subsolvers) that use the feasibility jump code to find improving solution, treating the objective improvement as a hard constraint.
`int`	`getNumWorkers()`	Specify the number of parallel workers (i.e.
`boolean`	`getOnlyAddCutsAtLevelZero()`	For the cut that can be generated at any level, this control if we only try to generate them at the root node.
`boolean`	`getOnlySolveIp()`	If one try to solve a MIP model with CP-SAT, because we assume all variable to be integer after scaling, we will not necessarily have the correct optimal.
`boolean`	`getOptimizeWithCore()`	The default optimization method is a simple "linear scan", each time trying to find a better solution than the previous one.
`boolean`	`getOptimizeWithLbTreeSearch()`	Do a more conventional tree search (by opposition to SAT based one) where we keep all the explored node in a tree.
`boolean`	`getOptimizeWithMaxHs()`	This has no effect if optimize_with_core is false.
`int`	`getPbCleanupIncrement()`	Same as for the clauses, but for the learned pseudo-Boolean constraints.
`double`	`getPbCleanupRatio()`	`optional double pb_cleanup_ratio = 47 [default = 0.5];`
`boolean`	`getPermutePresolveConstraintOrder()`	`optional bool permute_presolve_constraint_order = 179 [default = false];`
`boolean`	`getPermuteVariableRandomly()`	This is mainly here to test the solver variability.
`int`	`getPolarityRephaseIncrement()`	If non-zero, then we change the polarity heuristic after that many number of conflicts in an arithmetically increasing fashion.
`boolean`	`getPolishLpSolution()`	Whether we try to do a few degenerate iteration at the end of an LP solve to minimize the fractionality of the integer variable in the basis.
`SatParameters.VariableOrder`	`getPreferredVariableOrder()`	`optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];`
`boolean`	`getPresolveBlockedClause()`	Whether we use an heuristic to detect some basic case of blocked clause in the SAT presolve.
`int`	`getPresolveBvaThreshold()`	Apply Bounded Variable Addition (BVA) if the number of clauses is reduced by stricly more than this threshold.
`int`	`getPresolveBveClauseWeight()`	During presolve, we apply BVE only if this weight times the number of clauses plus the number of clause literals is not increased.
`int`	`getPresolveBveThreshold()`	During presolve, only try to perform the bounded variable elimination (BVE) of a variable x if the number of occurrences of x times the number of occurrences of not(x) is not greater than this parameter.
`boolean`	`getPresolveExtractIntegerEnforcement()`	If true, we will extract from linear constraints, enforcement literals of the form "integer variable at bound => simplified constraint".
`long`	`getPresolveInclusionWorkLimit()`	A few presolve operations involve detecting constraints included in other constraint.
`double`	`getPresolveProbingDeterministicTimeLimit()`	`optional double presolve_probing_deterministic_time_limit = 57 [default = 30];`
`int`	`getPresolveSubstitutionLevel()`	How much substitution (also called free variable aggregation in MIP litterature) should we perform at presolve.
`boolean`	`getPresolveUseBva()`	Whether or not we use Bounded Variable Addition (BVA) in the presolve.
`double`	`getProbingDeterministicTimeLimit()`	The maximum "deterministic" time limit to spend in probing.
`long`	`getProbingPeriodAtRoot()`	If set at zero (the default), it is disabled.
`double`	`getPropagationLoopDetectionFactor()`	Some search decisions might cause a really large number of propagations to happen when integer variables with large domains are only reduced by 1 at each step.
`long`	`getPseudoCostReliabilityThreshold()`	The solver ignores the pseudo costs of variables with number of recordings less than this threshold.
`boolean`	`getPushAllTasksTowardStart()`	Experimental code: specify if the objective pushes all tasks toward the start of the schedule.
`double`	`getRandomBranchesRatio()`	A number between 0 and 1 that indicates the proportion of branching variables that are selected randomly instead of choosing the first variable from the given variable_ordering strategy.
`boolean`	`getRandomizeSearch()`	Randomize fixed search.
`double`	`getRandomPolarityRatio()`	The proportion of polarity chosen at random.
`int`	`getRandomSeed()`	At the beginning of each solve, the random number generator used in some part of the solver is reinitialized to this seed.
`double`	`getRelativeGapLimit()`	`optional double relative_gap_limit = 160 [default = 0];`
`boolean`	`getRepairHint()`	If true, the solver tries to repair the solution given in the hint.
`SatParameters.RestartAlgorithm`	`getRestartAlgorithms(int index)`	The restart strategies will change each time the strategy_counter is increased.
`int`	`getRestartAlgorithmsCount()`	The restart strategies will change each time the strategy_counter is increased.
`java.util.List<SatParameters.RestartAlgorithm>`	`getRestartAlgorithmsList()`	The restart strategies will change each time the strategy_counter is increased.
`double`	`getRestartDlAverageRatio()`	In the moving average restart algorithms, a restart is triggered if the window average times this ratio is greater that the global average.
`double`	`getRestartLbdAverageRatio()`	`optional double restart_lbd_average_ratio = 71 [default = 1];`
`int`	`getRestartPeriod()`	Restart period for the FIXED_RESTART strategy.
`int`	`getRestartRunningWindowSize()`	Size of the window for the moving average restarts.
`int`	`getRootLpIterations()`	Even at the root node, we do not want to spend too much time on the LP if it is "difficult".
`SatParameters.SearchBranching`	`getSearchBranching()`	`optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];`
`long`	`getSearchRandomVariablePoolSize()`	Search randomization will collect the top 'search_random_variable_pool_size' valued variables, and pick one randomly.
`boolean`	`getShareBinaryClauses()`	Allows sharing of new learned binary clause between workers.
`int`	`getSharedTreeMaxNodesPerWorker()`	In order to limit total shared memory and communication overhead, limit the total number of nodes that may be generated in the shared tree.
`int`	`getSharedTreeNumWorkers()`	Enables experimental workstealing-like shared tree search.
`SatParameters.SharedTreeSplitStrategy`	`getSharedTreeSplitStrategy()`	`optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];`
`double`	`getSharedTreeWorkerObjectiveSplitProbability()`	After their assigned prefix, shared tree workers will branch on the objective with this probability.
`boolean`	`getShareLevelZeroBounds()`	Allows sharing of the bounds of modified variables at level 0.
`boolean`	`getShareObjectiveBounds()`	Allows objective sharing between workers.
`double`	`getShavingSearchDeterministicTime()`	Specifies the amount of deterministic time spent of each try at shaving a bound in the shaving search.
`int`	`getSolutionPoolSize()`	Size of the top-n different solutions kept by the solver.
`boolean`	`getStopAfterFirstSolution()`	For an optimization problem, stop the solver as soon as we have a solution.
`boolean`	`getStopAfterPresolve()`	Mainly used when improving the presolver.
`boolean`	`getStopAfterRootPropagation()`	`optional bool stop_after_root_propagation = 252 [default = false];`
`double`	`getStrategyChangeIncreaseRatio()`	The parameter num_conflicts_before_strategy_changes is increased by that much after each strategy change.
`SatParameters`	`getSubsolverParams(int index)`	It is possible to specify additional subsolver configuration.
`int`	`getSubsolverParamsCount()`	It is possible to specify additional subsolver configuration.
`java.util.List<SatParameters>`	`getSubsolverParamsList()`	It is possible to specify additional subsolver configuration.
`SatParametersOrBuilder`	`getSubsolverParamsOrBuilder(int index)`	It is possible to specify additional subsolver configuration.
`java.util.List<? extends SatParametersOrBuilder>`	`getSubsolverParamsOrBuilderList()`	It is possible to specify additional subsolver configuration.
`java.lang.String`	`getSubsolvers(int index)`	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
`com.google.protobuf.ByteString`	`getSubsolversBytes(int index)`	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
`int`	`getSubsolversCount()`	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
`java.util.List<java.lang.String>`	`getSubsolversList()`	In multi-thread, the solver can be mainly seen as a portfolio of solvers with different parameters.
`boolean`	`getSubsumptionDuringConflictAnalysis()`	At a really low cost, during the 1-UIP conflict computation, it is easy to detect if some of the involved reasons are subsumed by the current conflict.
`int`	`getSymmetryLevel()`	Whether we try to automatically detect the symmetries in a model and exploit them.
`int`	`getTableCompressionLevel()`	How much we try to "compress" a table constraint.
`boolean`	`getTestFeasibilityJump()`	Disable every other type of subsolver, setting this turns CP-SAT into a pure local-search solver.
`boolean`	`getUseAbslRandom()`	`optional bool use_absl_random = 180 [default = false];`
`boolean`	`getUseBlockingRestart()`	Block a moving restart algorithm if the trail size of the current conflict is greater than the multiplier times the moving average of the trail size at the previous conflicts.
`boolean`	`getUseBranchingInLp()`	If true, the solver attemts to generate more info inside lp propagator by branching on some variables if certain criteria are met during the search tree exploration.
`boolean`	`getUseCombinedNoOverlap()`	This can be beneficial if there is a lot of no-overlap constraints but a relatively low number of different intervals in the problem.
`boolean`	`getUseDisjunctiveConstraintInCumulative()`	When this is true, the cumulative constraint is reinforced with propagators from the disjunctive constraint to improve the inference on a set of tasks that are disjunctive at the root of the problem.
`boolean`	`getUseDualSchedulingHeuristics()`	When set, it activates a few scheduling parameters to improve the lower bound of scheduling problems.
`boolean`	`getUseDynamicPrecedenceInCumulative()`	`optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];`
`boolean`	`getUseDynamicPrecedenceInDisjunctive()`	Whether we try to branch on decision "interval A before interval B" rather than on intervals bounds.
`boolean`	`getUseEnergeticReasoningInNoOverlap2D()`	When this is true, the no_overlap_2d constraint is reinforced with energetic reasoning.
`boolean`	`getUseErwaHeuristic()`	Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as described in "Learning Rate Based Branching Heuristic for SAT solvers", J.H.Liang, V.
`boolean`	`getUseExactLpReason()`	The solver usually exploit the LP relaxation of a model.
`boolean`	`getUseFeasibilityJump()`	Parameters for an heuristic similar to the one described in the paper: "Feasibility Jump: an LP-free Lagrangian MIP heuristic", Bjørnar Luteberget, Giorgio Sartor, 2023, Mathematical Programming Computation.
`boolean`	`getUseFeasibilityPump()`	Adds a feasibility pump subsolver along with lns subsolvers.
`boolean`	`getUseHardPrecedencesInCumulative()`	If true, detect and create constraint for integer variable that are "after" a set of intervals in the same cumulative constraint.
`boolean`	`getUseImpliedBounds()`	Stores and exploits "implied-bounds" in the solver.
`boolean`	`getUseLbRelaxLns()`	Turns on neighborhood generator based on local branching LP.
`boolean`	`getUseLnsOnly()`	LNS parameters.
`boolean`	`getUseObjectiveLbSearch()`	If true, search will search in ascending max objective value (when minimizing) starting from the lower bound of the objective.
`boolean`	`getUseObjectiveShavingSearch()`	This search differs from the previous search as it will not use assumptions to bound the objective, and it will recreate a full model with the hardcoded objective value.
`boolean`	`getUseOptimizationHints()`	For an optimization problem, whether we follow some hints in order to find a better first solution.
`boolean`	`getUseOptionalVariables()`	If true, we automatically detect variables whose constraint are always enforced by the same literal and we mark them as optional.
`boolean`	`getUseOverloadCheckerInCumulative()`	When this is true, the cumulative constraint is reinforced with overload checking, i.e., an additional level of reasoning based on energy.
`boolean`	`getUsePairwiseReasoningInNoOverlap2D()`	Performs an extra step of propagation in the no_overlap_2d constraint by looking at all pairs of intervals.
`boolean`	`getUsePbResolution()`	Whether to use pseudo-Boolean resolution to analyze a conflict.
`boolean`	`getUsePhaseSaving()`	If this is true, then the polarity of a variable will be the last value it was assigned to, or its default polarity if it was never assigned since the call to ResetDecisionHeuristic().
`boolean`	`getUsePrecedencesInDisjunctiveConstraint()`	When this is true, then a disjunctive constraint will try to use the precedence relations between time intervals to propagate their bounds further.
`boolean`	`getUseProbingSearch()`	If true, search will continuously probe Boolean variables, and integer variable bounds.
`boolean`	`getUseRinsLns()`	Turns on relaxation induced neighborhood generator.
`boolean`	`getUseSatInprocessing()`	`optional bool use_sat_inprocessing = 163 [default = false];`
`boolean`	`getUseSharedTreeSearch()`	Set on shared subtree workers.
`boolean`	`getUseShavingInProbingSearch()`	Add a shaving phase (where the solver tries to prove that the lower or upper bound of a variable are infeasible) to the probing search.
`boolean`	`getUseStrongPropagationInDisjunctive()`	Enable stronger and more expensive propagation on no_overlap constraint.
`boolean`	`getUseTimetableEdgeFindingInCumulative()`	When this is true, the cumulative constraint is reinforced with timetable edge finding, i.e., an additional level of reasoning based on the conjunction of energy and mandatory parts.
`boolean`	`getUseTimetablingInNoOverlap2D()`	When this is true, the no_overlap_2d constraint is reinforced with propagators from the cumulative constraints.
`double`	`getVariableActivityDecay()`	Each time a conflict is found, the activities of some variables are increased by one.
`double`	`getViolationLsCompoundMoveProbability()`	Probability of using compound move search each restart.
`int`	`getViolationLsPerturbationPeriod()`	How long violation_ls should wait before perturbating a solution.
`boolean`	`hasAbsoluteGapLimit()`	Stop the search when the gap between the best feasible objective (O) and our best objective bound (B) is smaller than a limit.
`boolean`	`hasAddCgCuts()`	Whether we generate and add Chvatal-Gomory cuts to the LP at root node.
`boolean`	`hasAddCliqueCuts()`	Whether we generate clique cuts from the binary implication graph.
`boolean`	`hasAddLinMaxCuts()`	For the lin max constraints, generates the cuts described in "Strong mixed-integer programming formulations for trained neural networks" by Ross Anderson et.
`boolean`	`hasAddLpConstraintsLazily()`	If true, we start by an empty LP, and only add constraints not satisfied by the current LP solution batch by batch.
`boolean`	`hasAddMirCuts()`	Whether we generate MIR cuts at root node.
`boolean`	`hasAddObjectiveCut()`	When the LP objective is fractional, do we add the cut that forces the linear objective expression to be greater or equal to this fractional value rounded up? We can always do that since our objective is integer, and combined with MIR heuristic to reduce the coefficient of such cut, it can help.
`boolean`	`hasAddZeroHalfCuts()`	Whether we generate Zero-Half cuts at root node.
`boolean`	`hasAlsoBumpVariablesInConflictReasons()`	When this is true, then the variables that appear in any of the reason of the variables in a conflict have their activity bumped.
`boolean`	`hasAutoDetectGreaterThanAtLeastOneOf()`	If true, then the precedences propagator try to detect for each variable if it has a set of "optional incoming arc" for which at least one of them is present.
`boolean`	`hasBinaryMinimizationAlgorithm()`	`optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FIRST];`
`boolean`	`hasBinarySearchNumConflicts()`	If non-negative, perform a binary search on the objective variable in order to find an [min, max] interval outside of which the solver proved unsat/sat under this amount of conflict.
`boolean`	`hasBlockingRestartMultiplier()`	`optional double blocking_restart_multiplier = 66 [default = 1.4];`
`boolean`	`hasBlockingRestartWindowSize()`	`optional int32 blocking_restart_window_size = 65 [default = 5000];`
`boolean`	`hasBooleanEncodingLevel()`	A non-negative level indicating how much we should try to fully encode Integer variables as Boolean.
`boolean`	`hasCatchSigintSignal()`	Indicates if the CP-SAT layer should catch Control-C (SIGINT) signals when calling solve.
`boolean`	`hasClauseActivityDecay()`	Clause activity parameters (same effect as the one on the variables).
`boolean`	`hasClauseCleanupLbdBound()`	All the clauses with a LBD (literal blocks distance) lower or equal to this parameters will always be kept.
`boolean`	`hasClauseCleanupOrdering()`	`optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];`
`boolean`	`hasClauseCleanupPeriod()`	Trigger a cleanup when this number of "deletable" clauses is learned.
`boolean`	`hasClauseCleanupProtection()`	`optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE];`
`boolean`	`hasClauseCleanupRatio()`	During a cleanup, if clause_cleanup_target is 0, we will delete the clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed target of clauses to keep.
`boolean`	`hasClauseCleanupTarget()`	During a cleanup, we will always keep that number of "deletable" clauses.
`boolean`	`hasConvertIntervals()`	Temporary flag util the feature is more mature.
`boolean`	`hasCoreMinimizationLevel()`	If positive, we spend some effort on each core: - At level 1, we use a simple heuristic to try to minimize an UNSAT core.
`boolean`	`hasCountAssumptionLevelsInLbd()`	Whether or not the assumption levels are taken into account during the LBD computation.
`boolean`	`hasCoverOptimization()`	If true, when the max-sat algo find a core, we compute the minimal number of literals in the core that needs to be true to have a feasible solution.
`boolean`	`hasCpModelPresolve()`	Whether we presolve the cp_model before solving it.
`boolean`	`hasCpModelProbingLevel()`	How much effort do we spend on probing.
`boolean`	`hasCpModelUseSatPresolve()`	Whether we also use the sat presolve when cp_model_presolve is true.
`boolean`	`hasCutActiveCountDecay()`	`optional double cut_active_count_decay = 156 [default = 0.8];`
`boolean`	`hasCutCleanupTarget()`	Target number of constraints to remove during cleanup.
`boolean`	`hasCutLevel()`	Control the global cut effort.
`boolean`	`hasCutMaxActiveCountValue()`	These parameters are similar to sat clause management activity parameters.
`boolean`	`hasDebugCrashOnBadHint()`	Crash if we do not manage to complete the hint into a full solution.
`boolean`	`hasDebugMaxNumPresolveOperations()`	If positive, try to stop just after that many presolve rules have been applied.
`boolean`	`hasDebugPostsolveWithFullSolver()`	We have two different postsolve code.
`boolean`	`hasDefaultRestartAlgorithms()`	`optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];`
`boolean`	`hasDetectTableWithCost()`	If true, we detect variable that are unique to a table constraint and only there to encode a cost on each tuple.
`boolean`	`hasDisableConstraintExpansion()`	If true, it disable all constraint expansion.
`boolean`	`hasDiversifyLnsParams()`	If true, registers more lns subsolvers with different parameters.
`boolean`	`hasEncodeComplexLinearConstraintWithInteger()`	Linear constraint with a complex right hand side (more than a single interval) need to be expanded, there is a couple of way to do that.
`boolean`	`hasEnumerateAllSolutions()`	Whether we enumerate all solutions of a problem without objective.
`boolean`	`hasExpandAlldiffConstraints()`	If true, expand all_different constraints that are not permutations.
`boolean`	`hasExpandReservoirConstraints()`	If true, expand the reservoir constraints by creating booleans for all possible precedences between event and encoding the constraint.
`boolean`	`hasExploitAllLpSolution()`	If true and the Lp relaxation of the problem has a solution, try to exploit it.
`boolean`	`hasExploitAllPrecedences()`	`optional bool exploit_all_precedences = 220 [default = false];`
`boolean`	`hasExploitBestSolution()`	When branching on a variable, follow the last best solution value.
`boolean`	`hasExploitIntegerLpSolution()`	If true and the Lp relaxation of the problem has an integer optimal solution, try to exploit it.
`boolean`	`hasExploitObjective()`	When branching an a variable that directly affect the objective, branch on the value that lead to the best objective first.
`boolean`	`hasExploitRelaxationSolution()`	When branching on a variable, follow the last best relaxation solution value.
`boolean`	`hasFeasibilityJumpDecay()`	On each restart, we randomly choose if we use decay (with this parameter) or no decay.
`boolean`	`hasFeasibilityJumpEnableRestarts()`	When stagnating, feasibility jump will either restart from a default solution (with some possible randomization), or randomly pertubate the current solution.
`boolean`	`hasFeasibilityJumpLinearizationLevel()`	How much do we linearize the problem in the local search code.
`boolean`	`hasFeasibilityJumpMaxExpandedConstraintSize()`	Maximum size of no_overlap or no_overlap_2d constraint for a quadratic expansion.
`boolean`	`hasFeasibilityJumpRestartFactor()`	This is a factor that directly influence the work before each restart.
`boolean`	`hasFeasibilityJumpVarPerburbationRangeRatio()`	Max distance between the default value and the pertubated value relative to the range of the domain of the variable.
`boolean`	`hasFeasibilityJumpVarRandomizationProbability()`	Probability for a variable to have a non default value upon restarts or perturbations.
`boolean`	`hasFillAdditionalSolutionsInResponse()`	If true, the final response addition_solutions field will be filled with all solutions from our solutions pool.
`boolean`	`hasFillTightenedDomainsInResponse()`	If true, add information about the derived variable domains to the CpSolverResponse.
`boolean`	`hasFindBigLinearOverlap()`	Try to find large "rectangle" in the linear constraint matrix with identical lines.
`boolean`	`hasFindMultipleCores()`	Whether we try to find more independent cores for a given set of assumptions in the core based max-SAT algorithms.
`boolean`	`hasFixVariablesToTheirHintedValue()`	If true, variables appearing in the solution hints will be fixed to their hinted value.
`boolean`	`hasFpRounding()`	`optional .operations_research.sat.SatParameters.FPRoundingMethod fp_rounding = 165 [default = PROPAGATION_ASSISTED];`
`boolean`	`hasGlucoseDecayIncrement()`	`optional double glucose_decay_increment = 23 [default = 0.01];`
`boolean`	`hasGlucoseDecayIncrementPeriod()`	`optional int32 glucose_decay_increment_period = 24 [default = 5000];`
`boolean`	`hasGlucoseMaxDecay()`	The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until 0.95.
`boolean`	`hasHintConflictLimit()`	Conflict limit used in the phase that exploit the solution hint.
`boolean`	`hasIgnoreNames()`	If true, we don't keep names in our internal copy of the user given model.
`boolean`	`hasInferAllDiffs()`	Run a max-clique code amongst all the x != y we can find and try to infer set of variables that are all different.
`boolean`	`hasInitialPolarity()`	`optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];`
`boolean`	`hasInitialVariablesActivity()`	The initial value of the variables activity.
`boolean`	`hasInstantiateAllVariables()`	If true, the solver will add a default integer branching strategy to the already defined search strategy.
`boolean`	`hasInterleaveBatchSize()`	`optional int32 interleave_batch_size = 134 [default = 0];`
`boolean`	`hasInterleaveSearch()`	Experimental.
`boolean`	`hasKeepAllFeasibleSolutionsInPresolve()`	If true, we disable the presolve reductions that remove feasible solutions from the search space.
`boolean`	`hasLinearizationLevel()`	A non-negative level indicating the type of constraints we consider in the LP relaxation.
`boolean`	`hasLinearSplitSize()`	Linear constraints that are not pseudo-Boolean and that are longer than this size will be split into sqrt(size) intermediate sums in order to have faster propation in the CP engine.
`boolean`	`hasLogPrefix()`	Add a prefix to all logs.
`boolean`	`hasLogSearchProgress()`	Whether the solver should log the search progress.
`boolean`	`hasLogSubsolverStatistics()`	Whether the solver should display per sub-solver search statistics.
`boolean`	`hasLogToResponse()`	Log to response proto.
`boolean`	`hasLogToStdout()`	Log to stdout.
`boolean`	`hasLpDualTolerance()`	`optional double lp_dual_tolerance = 267 [default = 1e-07];`
`boolean`	`hasLpPrimalTolerance()`	The internal LP tolerances used by CP-SAT.
`boolean`	`hasMaxAllDiffCutSize()`	Cut generator for all diffs can add too many cuts for large all_diff constraints.
`boolean`	`hasMaxClauseActivityValue()`	`optional double max_clause_activity_value = 18 [default = 1e+20];`
`boolean`	`hasMaxConsecutiveInactiveCount()`	If a constraint/cut in LP is not active for that many consecutive OPTIMAL solves, remove it from the LP.
`boolean`	`hasMaxCutRoundsAtLevelZero()`	Max number of time we perform cut generation and resolve the LP at level 0.
`boolean`	`hasMaxDeterministicTime()`	Maximum time allowed in deterministic time to solve a problem.
`boolean`	`hasMaxDomainSizeWhenEncodingEqNeqConstraints()`	When loading ax + by ==/!= c when x and y are both fully encoded.
`boolean`	`hasMaxIntegerRoundingScaling()`	In the integer rounding procedure used for MIR and Gomory cut, the maximum "scaling" we use (must be positive).
`boolean`	`hasMaxMemoryInMb()`	Maximum memory allowed for the whole thread containing the solver.
`boolean`	`hasMaxNumberOfConflicts()`	Maximum number of conflicts allowed to solve a problem.
`boolean`	`hasMaxNumCuts()`	The limit on the number of cuts in our cut pool.
`boolean`	`hasMaxNumIntervalsForTimetableEdgeFinding()`	Max number of intervals for the timetable_edge_finding algorithm to propagate.
`boolean`	`hasMaxPresolveIterations()`	In case of large reduction in a presolve iteration, we perform multiple presolve iterations.
`boolean`	`hasMaxSatAssumptionOrder()`	`optional .operations_research.sat.SatParameters.MaxSatAssumptionOrder max_sat_assumption_order = 51 [default = DEFAULT_ASSUMPTION_ORDER];`
`boolean`	`hasMaxSatReverseAssumptionOrder()`	If true, adds the assumption in the reverse order of the one defined by max_sat_assumption_order.
`boolean`	`hasMaxSatStratification()`	`optional .operations_research.sat.SatParameters.MaxSatStratificationAlgorithm max_sat_stratification = 53 [default = STRATIFICATION_DESCENT];`
`boolean`	`hasMaxSizeToCreatePrecedenceLiteralsInDisjunctive()`	Create one literal for each disjunction of two pairs of tasks.
`boolean`	`hasMaxTimeInSeconds()`	Maximum time allowed in seconds to solve a problem.
`boolean`	`hasMaxVariableActivityValue()`	`optional double max_variable_activity_value = 16 [default = 1e+100];`
`boolean`	`hasMergeAtMostOneWorkLimit()`	`optional double merge_at_most_one_work_limit = 146 [default = 100000000];`
`boolean`	`hasMergeNoOverlapWorkLimit()`	During presolve, we use a maximum clique heuristic to merge together no-overlap constraints or at most one constraints.
`boolean`	`hasMinimizationAlgorithm()`	`optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];`
`boolean`	`hasMinimizeReductionDuringPbResolution()`	A different algorithm during PB resolution.
`boolean`	`hasMinimizeWithPropagationNumDecisions()`	`optional int32 minimize_with_propagation_num_decisions = 97 [default = 1000];`
`boolean`	`hasMinimizeWithPropagationRestartPeriod()`	Parameters for an heuristic similar to the one described in "An effective learnt clause minimization approach for CDCL Sat Solvers", https://www.ijcai.org/proceedings/2017/0098.pdf For now, we have a somewhat simpler implementation where every x restart we spend y decisions on clause minimization.
`boolean`	`hasMinNumLnsWorkers()`	Obsolete parameter.
`boolean`	`hasMinOrthogonalityForLpConstraints()`	While adding constraints, skip the constraints which have orthogonality less than 'min_orthogonality_for_lp_constraints' with already added constraints during current call.
`boolean`	`hasMipAutomaticallyScaleVariables()`	If true, some continuous variable might be automatically scaled.
`boolean`	`hasMipCheckPrecision()`	As explained in mip_precision and mip_max_activity_exponent, we cannot always reach the wanted precision during scaling.
`boolean`	`hasMipComputeTrueObjectiveBound()`	Even if we make big error when scaling the objective, we can always derive a correct lower bound on the original objective by using the exact lower bound on the scaled integer version of the objective.
`boolean`	`hasMipDropTolerance()`	Any value in the input mip with a magnitude lower than this will be set to zero.
`boolean`	`hasMipMaxActivityExponent()`	To avoid integer overflow, we always force the maximum possible constraint activity (and objective value) according to the initial variable domain to be smaller than 2 to this given power.
`boolean`	`hasMipMaxBound()`	We need to bound the maximum magnitude of the variables for CP-SAT, and that is the bound we use.
`boolean`	`hasMipMaxValidMagnitude()`	Any finite values in the input MIP must be below this threshold, otherwise the model will be reported invalid.
`boolean`	`hasMipPresolveLevel()`	When solving a MIP, we do some basic floating point presolving before scaling the problem to integer to be handled by CP-SAT.
`boolean`	`hasMipScaleLargeDomain()`	If this is false, then mip_var_scaling is only applied to variables with "small" domain.
`boolean`	`hasMipVarScaling()`	All continuous variable of the problem will be multiplied by this factor.
`boolean`	`hasMipWantedPrecision()`	When scaling constraint with double coefficients to integer coefficients, we will multiply by a power of 2 and round the coefficients.
`boolean`	`hasName()`	In some context, like in a portfolio of search, it makes sense to name a given parameters set for logging purpose.
`boolean`	`hasNewConstraintsBatchSize()`	Add that many lazy constraints (or cuts) at once in the LP.
`boolean`	`hasNewLinearPropagation()`	Experimental.
`boolean`	`hasNumConflictsBeforeStrategyChanges()`	After each restart, if the number of conflict since the last strategy change is greater that this, then we increment a "strategy_counter" that can be use to change the search strategy used by the following restarts.
`boolean`	`hasNumSearchWorkers()`	`optional int32 num_search_workers = 100 [default = 0];`
`boolean`	`hasNumViolationLs()`	This will create incomplete subsolvers (that are not LNS subsolvers) that use the feasibility jump code to find improving solution, treating the objective improvement as a hard constraint.
`boolean`	`hasNumWorkers()`	Specify the number of parallel workers (i.e.
`boolean`	`hasOnlyAddCutsAtLevelZero()`	For the cut that can be generated at any level, this control if we only try to generate them at the root node.
`boolean`	`hasOnlySolveIp()`	If one try to solve a MIP model with CP-SAT, because we assume all variable to be integer after scaling, we will not necessarily have the correct optimal.
`boolean`	`hasOptimizeWithCore()`	The default optimization method is a simple "linear scan", each time trying to find a better solution than the previous one.
`boolean`	`hasOptimizeWithLbTreeSearch()`	Do a more conventional tree search (by opposition to SAT based one) where we keep all the explored node in a tree.
`boolean`	`hasOptimizeWithMaxHs()`	This has no effect if optimize_with_core is false.
`boolean`	`hasPbCleanupIncrement()`	Same as for the clauses, but for the learned pseudo-Boolean constraints.
`boolean`	`hasPbCleanupRatio()`	`optional double pb_cleanup_ratio = 47 [default = 0.5];`
`boolean`	`hasPermutePresolveConstraintOrder()`	`optional bool permute_presolve_constraint_order = 179 [default = false];`
`boolean`	`hasPermuteVariableRandomly()`	This is mainly here to test the solver variability.
`boolean`	`hasPolarityRephaseIncrement()`	If non-zero, then we change the polarity heuristic after that many number of conflicts in an arithmetically increasing fashion.
`boolean`	`hasPolishLpSolution()`	Whether we try to do a few degenerate iteration at the end of an LP solve to minimize the fractionality of the integer variable in the basis.
`boolean`	`hasPreferredVariableOrder()`	`optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];`
`boolean`	`hasPresolveBlockedClause()`	Whether we use an heuristic to detect some basic case of blocked clause in the SAT presolve.
`boolean`	`hasPresolveBvaThreshold()`	Apply Bounded Variable Addition (BVA) if the number of clauses is reduced by stricly more than this threshold.
`boolean`	`hasPresolveBveClauseWeight()`	During presolve, we apply BVE only if this weight times the number of clauses plus the number of clause literals is not increased.
`boolean`	`hasPresolveBveThreshold()`	During presolve, only try to perform the bounded variable elimination (BVE) of a variable x if the number of occurrences of x times the number of occurrences of not(x) is not greater than this parameter.
`boolean`	`hasPresolveExtractIntegerEnforcement()`	If true, we will extract from linear constraints, enforcement literals of the form "integer variable at bound => simplified constraint".
`boolean`	`hasPresolveInclusionWorkLimit()`	A few presolve operations involve detecting constraints included in other constraint.
`boolean`	`hasPresolveProbingDeterministicTimeLimit()`	`optional double presolve_probing_deterministic_time_limit = 57 [default = 30];`
`boolean`	`hasPresolveSubstitutionLevel()`	How much substitution (also called free variable aggregation in MIP litterature) should we perform at presolve.
`boolean`	`hasPresolveUseBva()`	Whether or not we use Bounded Variable Addition (BVA) in the presolve.
`boolean`	`hasProbingDeterministicTimeLimit()`	The maximum "deterministic" time limit to spend in probing.
`boolean`	`hasProbingPeriodAtRoot()`	If set at zero (the default), it is disabled.
`boolean`	`hasPropagationLoopDetectionFactor()`	Some search decisions might cause a really large number of propagations to happen when integer variables with large domains are only reduced by 1 at each step.
`boolean`	`hasPseudoCostReliabilityThreshold()`	The solver ignores the pseudo costs of variables with number of recordings less than this threshold.
`boolean`	`hasPushAllTasksTowardStart()`	Experimental code: specify if the objective pushes all tasks toward the start of the schedule.
`boolean`	`hasRandomBranchesRatio()`	A number between 0 and 1 that indicates the proportion of branching variables that are selected randomly instead of choosing the first variable from the given variable_ordering strategy.
`boolean`	`hasRandomizeSearch()`	Randomize fixed search.
`boolean`	`hasRandomPolarityRatio()`	The proportion of polarity chosen at random.
`boolean`	`hasRandomSeed()`	At the beginning of each solve, the random number generator used in some part of the solver is reinitialized to this seed.
`boolean`	`hasRelativeGapLimit()`	`optional double relative_gap_limit = 160 [default = 0];`
`boolean`	`hasRepairHint()`	If true, the solver tries to repair the solution given in the hint.
`boolean`	`hasRestartDlAverageRatio()`	In the moving average restart algorithms, a restart is triggered if the window average times this ratio is greater that the global average.
`boolean`	`hasRestartLbdAverageRatio()`	`optional double restart_lbd_average_ratio = 71 [default = 1];`
`boolean`	`hasRestartPeriod()`	Restart period for the FIXED_RESTART strategy.
`boolean`	`hasRestartRunningWindowSize()`	Size of the window for the moving average restarts.
`boolean`	`hasRootLpIterations()`	Even at the root node, we do not want to spend too much time on the LP if it is "difficult".
`boolean`	`hasSearchBranching()`	`optional .operations_research.sat.SatParameters.SearchBranching search_branching = 82 [default = AUTOMATIC_SEARCH];`
`boolean`	`hasSearchRandomVariablePoolSize()`	Search randomization will collect the top 'search_random_variable_pool_size' valued variables, and pick one randomly.
`boolean`	`hasShareBinaryClauses()`	Allows sharing of new learned binary clause between workers.
`boolean`	`hasSharedTreeMaxNodesPerWorker()`	In order to limit total shared memory and communication overhead, limit the total number of nodes that may be generated in the shared tree.
`boolean`	`hasSharedTreeNumWorkers()`	Enables experimental workstealing-like shared tree search.
`boolean`	`hasSharedTreeSplitStrategy()`	`optional .operations_research.sat.SatParameters.SharedTreeSplitStrategy shared_tree_split_strategy = 239 [default = SPLIT_STRATEGY_AUTO];`
`boolean`	`hasSharedTreeWorkerObjectiveSplitProbability()`	After their assigned prefix, shared tree workers will branch on the objective with this probability.
`boolean`	`hasShareLevelZeroBounds()`	Allows sharing of the bounds of modified variables at level 0.
`boolean`	`hasShareObjectiveBounds()`	Allows objective sharing between workers.
`boolean`	`hasShavingSearchDeterministicTime()`	Specifies the amount of deterministic time spent of each try at shaving a bound in the shaving search.
`boolean`	`hasSolutionPoolSize()`	Size of the top-n different solutions kept by the solver.
`boolean`	`hasStopAfterFirstSolution()`	For an optimization problem, stop the solver as soon as we have a solution.
`boolean`	`hasStopAfterPresolve()`	Mainly used when improving the presolver.
`boolean`	`hasStopAfterRootPropagation()`	`optional bool stop_after_root_propagation = 252 [default = false];`
`boolean`	`hasStrategyChangeIncreaseRatio()`	The parameter num_conflicts_before_strategy_changes is increased by that much after each strategy change.
`boolean`	`hasSubsumptionDuringConflictAnalysis()`	At a really low cost, during the 1-UIP conflict computation, it is easy to detect if some of the involved reasons are subsumed by the current conflict.
`boolean`	`hasSymmetryLevel()`	Whether we try to automatically detect the symmetries in a model and exploit them.
`boolean`	`hasTableCompressionLevel()`	How much we try to "compress" a table constraint.
`boolean`	`hasTestFeasibilityJump()`	Disable every other type of subsolver, setting this turns CP-SAT into a pure local-search solver.
`boolean`	`hasUseAbslRandom()`	`optional bool use_absl_random = 180 [default = false];`
`boolean`	`hasUseBlockingRestart()`	Block a moving restart algorithm if the trail size of the current conflict is greater than the multiplier times the moving average of the trail size at the previous conflicts.
`boolean`	`hasUseBranchingInLp()`	If true, the solver attemts to generate more info inside lp propagator by branching on some variables if certain criteria are met during the search tree exploration.
`boolean`	`hasUseCombinedNoOverlap()`	This can be beneficial if there is a lot of no-overlap constraints but a relatively low number of different intervals in the problem.
`boolean`	`hasUseDisjunctiveConstraintInCumulative()`	When this is true, the cumulative constraint is reinforced with propagators from the disjunctive constraint to improve the inference on a set of tasks that are disjunctive at the root of the problem.
`boolean`	`hasUseDualSchedulingHeuristics()`	When set, it activates a few scheduling parameters to improve the lower bound of scheduling problems.
`boolean`	`hasUseDynamicPrecedenceInCumulative()`	`optional bool use_dynamic_precedence_in_cumulative = 268 [default = false];`
`boolean`	`hasUseDynamicPrecedenceInDisjunctive()`	Whether we try to branch on decision "interval A before interval B" rather than on intervals bounds.
`boolean`	`hasUseEnergeticReasoningInNoOverlap2D()`	When this is true, the no_overlap_2d constraint is reinforced with energetic reasoning.
`boolean`	`hasUseErwaHeuristic()`	Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as described in "Learning Rate Based Branching Heuristic for SAT solvers", J.H.Liang, V.
`boolean`	`hasUseExactLpReason()`	The solver usually exploit the LP relaxation of a model.
`boolean`	`hasUseFeasibilityJump()`	Parameters for an heuristic similar to the one described in the paper: "Feasibility Jump: an LP-free Lagrangian MIP heuristic", Bjørnar Luteberget, Giorgio Sartor, 2023, Mathematical Programming Computation.
`boolean`	`hasUseFeasibilityPump()`	Adds a feasibility pump subsolver along with lns subsolvers.
`boolean`	`hasUseHardPrecedencesInCumulative()`	If true, detect and create constraint for integer variable that are "after" a set of intervals in the same cumulative constraint.
`boolean`	`hasUseImpliedBounds()`	Stores and exploits "implied-bounds" in the solver.
`boolean`	`hasUseLbRelaxLns()`	Turns on neighborhood generator based on local branching LP.
`boolean`	`hasUseLnsOnly()`	LNS parameters.
`boolean`	`hasUseObjectiveLbSearch()`	If true, search will search in ascending max objective value (when minimizing) starting from the lower bound of the objective.
`boolean`	`hasUseObjectiveShavingSearch()`	This search differs from the previous search as it will not use assumptions to bound the objective, and it will recreate a full model with the hardcoded objective value.
`boolean`	`hasUseOptimizationHints()`	For an optimization problem, whether we follow some hints in order to find a better first solution.
`boolean`	`hasUseOptionalVariables()`	If true, we automatically detect variables whose constraint are always enforced by the same literal and we mark them as optional.
`boolean`	`hasUseOverloadCheckerInCumulative()`	When this is true, the cumulative constraint is reinforced with overload checking, i.e., an additional level of reasoning based on energy.
`boolean`	`hasUsePairwiseReasoningInNoOverlap2D()`	Performs an extra step of propagation in the no_overlap_2d constraint by looking at all pairs of intervals.
`boolean`	`hasUsePbResolution()`	Whether to use pseudo-Boolean resolution to analyze a conflict.
`boolean`	`hasUsePhaseSaving()`	If this is true, then the polarity of a variable will be the last value it was assigned to, or its default polarity if it was never assigned since the call to ResetDecisionHeuristic().
`boolean`	`hasUsePrecedencesInDisjunctiveConstraint()`	When this is true, then a disjunctive constraint will try to use the precedence relations between time intervals to propagate their bounds further.
`boolean`	`hasUseProbingSearch()`	If true, search will continuously probe Boolean variables, and integer variable bounds.
`boolean`	`hasUseRinsLns()`	Turns on relaxation induced neighborhood generator.
`boolean`	`hasUseSatInprocessing()`	`optional bool use_sat_inprocessing = 163 [default = false];`
`boolean`	`hasUseSharedTreeSearch()`	Set on shared subtree workers.
`boolean`	`hasUseShavingInProbingSearch()`	Add a shaving phase (where the solver tries to prove that the lower or upper bound of a variable are infeasible) to the probing search.
`boolean`	`hasUseStrongPropagationInDisjunctive()`	Enable stronger and more expensive propagation on no_overlap constraint.
`boolean`	`hasUseTimetableEdgeFindingInCumulative()`	When this is true, the cumulative constraint is reinforced with timetable edge finding, i.e., an additional level of reasoning based on the conjunction of energy and mandatory parts.
`boolean`	`hasUseTimetablingInNoOverlap2D()`	When this is true, the no_overlap_2d constraint is reinforced with propagators from the cumulative constraints.
`boolean`	`hasVariableActivityDecay()`	Each time a conflict is found, the activities of some variables are increased by one.
`boolean`	`hasViolationLsCompoundMoveProbability()`	Probability of using compound move search each restart.
`boolean`	`hasViolationLsPerturbationPeriod()`	How long violation_ls should wait before perturbating a solution.

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder
isInitialized

Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Method Detail

hasName

boolean hasName()

 In some context, like in a portfolio of search, it makes sense to name a
 given parameters set for logging purpose.

optional string name = 171 [default = ""];

Returns:: Whether the name field is set.

getName

java.lang.String getName()

 In some context, like in a portfolio of search, it makes sense to name a
 given parameters set for logging purpose.

optional string name = 171 [default = ""];

Returns:: The name.

getNameBytes

com.google.protobuf.ByteString getNameBytes()

 In some context, like in a portfolio of search, it makes sense to name a
 given parameters set for logging purpose.

optional string name = 171 [default = ""];

Returns:: The bytes for name.

hasPreferredVariableOrder
```
boolean hasPreferredVariableOrder()
```
optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];

Returns:

Whether the preferredVariableOrder field is set.

getPreferredVariableOrder
```
SatParameters.VariableOrder getPreferredVariableOrder()
```
optional .operations_research.sat.SatParameters.VariableOrder preferred_variable_order = 1 [default = IN_ORDER];

Returns:

The preferredVariableOrder.

hasInitialPolarity
```
boolean hasInitialPolarity()
```
optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];

Returns:

Whether the initialPolarity field is set.

getInitialPolarity
```
SatParameters.Polarity getInitialPolarity()
```
optional .operations_research.sat.SatParameters.Polarity initial_polarity = 2 [default = POLARITY_FALSE];

Returns:

The initialPolarity.

hasUsePhaseSaving

boolean hasUsePhaseSaving()

 If this is true, then the polarity of a variable will be the last value it
 was assigned to, or its default polarity if it was never assigned since the
 call to ResetDecisionHeuristic().

 Actually, we use a newer version where we follow the last value in the
 longest non-conflicting partial assignment in the current phase.

 This is called 'literal phase saving'. For details see 'A Lightweight
 Component Caching Scheme for Satisfiability Solvers' K. Pipatsrisawat and
 A.Darwiche, In 10th International Conference on Theory and Applications of
 Satisfiability Testing, 2007.

optional bool use_phase_saving = 44 [default = true];

Returns:: Whether the usePhaseSaving field is set.

getUsePhaseSaving

boolean getUsePhaseSaving()

 If this is true, then the polarity of a variable will be the last value it
 was assigned to, or its default polarity if it was never assigned since the
 call to ResetDecisionHeuristic().

 Actually, we use a newer version where we follow the last value in the
 longest non-conflicting partial assignment in the current phase.

 This is called 'literal phase saving'. For details see 'A Lightweight
 Component Caching Scheme for Satisfiability Solvers' K. Pipatsrisawat and
 A.Darwiche, In 10th International Conference on Theory and Applications of
 Satisfiability Testing, 2007.

optional bool use_phase_saving = 44 [default = true];

Returns:: The usePhaseSaving.

hasPolarityRephaseIncrement

boolean hasPolarityRephaseIncrement()

 If non-zero, then we change the polarity heuristic after that many number
 of conflicts in an arithmetically increasing fashion. So x the first time,
 2 * x the second time, etc...

optional int32 polarity_rephase_increment = 168 [default = 1000];

Returns:: Whether the polarityRephaseIncrement field is set.

getPolarityRephaseIncrement

int getPolarityRephaseIncrement()

 If non-zero, then we change the polarity heuristic after that many number
 of conflicts in an arithmetically increasing fashion. So x the first time,
 2 * x the second time, etc...

optional int32 polarity_rephase_increment = 168 [default = 1000];

Returns:: The polarityRephaseIncrement.

hasRandomPolarityRatio

boolean hasRandomPolarityRatio()

 The proportion of polarity chosen at random. Note that this take
 precedence over the phase saving heuristic. This is different from
 initial_polarity:POLARITY_RANDOM because it will select a new random
 polarity each time the variable is branched upon instead of selecting one
 initially and then always taking this choice.

optional double random_polarity_ratio = 45 [default = 0];

Returns:: Whether the randomPolarityRatio field is set.

getRandomPolarityRatio

double getRandomPolarityRatio()

 The proportion of polarity chosen at random. Note that this take
 precedence over the phase saving heuristic. This is different from
 initial_polarity:POLARITY_RANDOM because it will select a new random
 polarity each time the variable is branched upon instead of selecting one
 initially and then always taking this choice.

optional double random_polarity_ratio = 45 [default = 0];

Returns:: The randomPolarityRatio.

hasRandomBranchesRatio

boolean hasRandomBranchesRatio()

 A number between 0 and 1 that indicates the proportion of branching
 variables that are selected randomly instead of choosing the first variable
 from the given variable_ordering strategy.

optional double random_branches_ratio = 32 [default = 0];

Returns:: Whether the randomBranchesRatio field is set.

getRandomBranchesRatio

double getRandomBranchesRatio()

 A number between 0 and 1 that indicates the proportion of branching
 variables that are selected randomly instead of choosing the first variable
 from the given variable_ordering strategy.

optional double random_branches_ratio = 32 [default = 0];

Returns:: The randomBranchesRatio.

hasUseErwaHeuristic

boolean hasUseErwaHeuristic()

 Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as
 described in "Learning Rate Based Branching Heuristic for SAT solvers",
 J.H.Liang, V. Ganesh, P. Poupart, K.Czarnecki, SAT 2016.

optional bool use_erwa_heuristic = 75 [default = false];

Returns:: Whether the useErwaHeuristic field is set.

getUseErwaHeuristic

boolean getUseErwaHeuristic()

 Whether we use the ERWA (Exponential Recency Weighted Average) heuristic as
 described in "Learning Rate Based Branching Heuristic for SAT solvers",
 J.H.Liang, V. Ganesh, P. Poupart, K.Czarnecki, SAT 2016.

optional bool use_erwa_heuristic = 75 [default = false];

Returns:: The useErwaHeuristic.

hasInitialVariablesActivity

boolean hasInitialVariablesActivity()

 The initial value of the variables activity. A non-zero value only make
 sense when use_erwa_heuristic is true. Experiments with a value of 1e-2
 together with the ERWA heuristic showed slighthly better result than simply
 using zero. The idea is that when the "learning rate" of a variable becomes
 lower than this value, then we prefer to branch on never explored before
 variables. This is not in the ERWA paper.

optional double initial_variables_activity = 76 [default = 0];

Returns:: Whether the initialVariablesActivity field is set.

getInitialVariablesActivity

double getInitialVariablesActivity()

 The initial value of the variables activity. A non-zero value only make
 sense when use_erwa_heuristic is true. Experiments with a value of 1e-2
 together with the ERWA heuristic showed slighthly better result than simply
 using zero. The idea is that when the "learning rate" of a variable becomes
 lower than this value, then we prefer to branch on never explored before
 variables. This is not in the ERWA paper.

optional double initial_variables_activity = 76 [default = 0];

Returns:: The initialVariablesActivity.

hasAlsoBumpVariablesInConflictReasons

boolean hasAlsoBumpVariablesInConflictReasons()

 When this is true, then the variables that appear in any of the reason of
 the variables in a conflict have their activity bumped. This is addition to
 the variables in the conflict, and the one that were used during conflict
 resolution.

optional bool also_bump_variables_in_conflict_reasons = 77 [default = false];

Returns:: Whether the alsoBumpVariablesInConflictReasons field is set.

getAlsoBumpVariablesInConflictReasons

boolean getAlsoBumpVariablesInConflictReasons()

 When this is true, then the variables that appear in any of the reason of
 the variables in a conflict have their activity bumped. This is addition to
 the variables in the conflict, and the one that were used during conflict
 resolution.

optional bool also_bump_variables_in_conflict_reasons = 77 [default = false];

Returns:: The alsoBumpVariablesInConflictReasons.

hasMinimizationAlgorithm
```
boolean hasMinimizationAlgorithm()
```
optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];

Returns:

Whether the minimizationAlgorithm field is set.

getMinimizationAlgorithm
```
SatParameters.ConflictMinimizationAlgorithm getMinimizationAlgorithm()
```
optional .operations_research.sat.SatParameters.ConflictMinimizationAlgorithm minimization_algorithm = 4 [default = RECURSIVE];

Returns:

The minimizationAlgorithm.

hasBinaryMinimizationAlgorithm
```
boolean hasBinaryMinimizationAlgorithm()
```
optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FIRST];

Returns:

Whether the binaryMinimizationAlgorithm field is set.

getBinaryMinimizationAlgorithm
```
SatParameters.BinaryMinizationAlgorithm getBinaryMinimizationAlgorithm()
```
optional .operations_research.sat.SatParameters.BinaryMinizationAlgorithm binary_minimization_algorithm = 34 [default = BINARY_MINIMIZATION_FIRST];

Returns:

The binaryMinimizationAlgorithm.

hasSubsumptionDuringConflictAnalysis

boolean hasSubsumptionDuringConflictAnalysis()

 At a really low cost, during the 1-UIP conflict computation, it is easy to
 detect if some of the involved reasons are subsumed by the current
 conflict. When this is true, such clauses are detached and later removed
 from the problem.

optional bool subsumption_during_conflict_analysis = 56 [default = true];

Returns:: Whether the subsumptionDuringConflictAnalysis field is set.

getSubsumptionDuringConflictAnalysis

boolean getSubsumptionDuringConflictAnalysis()

 At a really low cost, during the 1-UIP conflict computation, it is easy to
 detect if some of the involved reasons are subsumed by the current
 conflict. When this is true, such clauses are detached and later removed
 from the problem.

optional bool subsumption_during_conflict_analysis = 56 [default = true];

Returns:: The subsumptionDuringConflictAnalysis.

hasClauseCleanupPeriod
```
boolean hasClauseCleanupPeriod()
```
```
 Trigger a cleanup when this number of "deletable" clauses is learned.
 
```
optional int32 clause_cleanup_period = 11 [default = 10000];
Returns:

Whether the clauseCleanupPeriod field is set.

getClauseCleanupPeriod
```
int getClauseCleanupPeriod()
```
```
 Trigger a cleanup when this number of "deletable" clauses is learned.
 
```
optional int32 clause_cleanup_period = 11 [default = 10000];
Returns:

The clauseCleanupPeriod.

hasClauseCleanupTarget

boolean hasClauseCleanupTarget()

 During a cleanup, we will always keep that number of "deletable" clauses.
 Note that this doesn't include the "protected" clauses.

optional int32 clause_cleanup_target = 13 [default = 0];

Returns:: Whether the clauseCleanupTarget field is set.

getClauseCleanupTarget

int getClauseCleanupTarget()

 During a cleanup, we will always keep that number of "deletable" clauses.
 Note that this doesn't include the "protected" clauses.

optional int32 clause_cleanup_target = 13 [default = 0];

Returns:: The clauseCleanupTarget.

hasClauseCleanupRatio

boolean hasClauseCleanupRatio()

 During a cleanup, if clause_cleanup_target is 0, we will delete the
 clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed
 target of clauses to keep.

optional double clause_cleanup_ratio = 190 [default = 0.5];

Returns:: Whether the clauseCleanupRatio field is set.

getClauseCleanupRatio

double getClauseCleanupRatio()

 During a cleanup, if clause_cleanup_target is 0, we will delete the
 clause_cleanup_ratio of "deletable" clauses instead of aiming for a fixed
 target of clauses to keep.

optional double clause_cleanup_ratio = 190 [default = 0.5];

Returns:: The clauseCleanupRatio.

hasClauseCleanupProtection
```
boolean hasClauseCleanupProtection()
```
optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE];

Returns:

Whether the clauseCleanupProtection field is set.

getClauseCleanupProtection
```
SatParameters.ClauseProtection getClauseCleanupProtection()
```
optional .operations_research.sat.SatParameters.ClauseProtection clause_cleanup_protection = 58 [default = PROTECTION_NONE];

Returns:

The clauseCleanupProtection.

hasClauseCleanupLbdBound

boolean hasClauseCleanupLbdBound()

 All the clauses with a LBD (literal blocks distance) lower or equal to this
 parameters will always be kept.

optional int32 clause_cleanup_lbd_bound = 59 [default = 5];

Returns:: Whether the clauseCleanupLbdBound field is set.

getClauseCleanupLbdBound

int getClauseCleanupLbdBound()

 All the clauses with a LBD (literal blocks distance) lower or equal to this
 parameters will always be kept.

optional int32 clause_cleanup_lbd_bound = 59 [default = 5];

Returns:: The clauseCleanupLbdBound.

hasClauseCleanupOrdering
```
boolean hasClauseCleanupOrdering()
```
optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];

Returns:

Whether the clauseCleanupOrdering field is set.

getClauseCleanupOrdering
```
SatParameters.ClauseOrdering getClauseCleanupOrdering()
```
optional .operations_research.sat.SatParameters.ClauseOrdering clause_cleanup_ordering = 60 [default = CLAUSE_ACTIVITY];

Returns:

The clauseCleanupOrdering.

hasPbCleanupIncrement
```
boolean hasPbCleanupIncrement()
```
```
 Same as for the clauses, but for the learned pseudo-Boolean constraints.
 
```
optional int32 pb_cleanup_increment = 46 [default = 200];
Returns:

Whether the pbCleanupIncrement field is set.

getPbCleanupIncrement
```
int getPbCleanupIncrement()
```
```
 Same as for the clauses, but for the learned pseudo-Boolean constraints.
 
```
optional int32 pb_cleanup_increment = 46 [default = 200];
Returns:

The pbCleanupIncrement.

hasPbCleanupRatio
```
boolean hasPbCleanupRatio()
```
optional double pb_cleanup_ratio = 47 [default = 0.5];

Returns:

Whether the pbCleanupRatio field is set.

getPbCleanupRatio
```
double getPbCleanupRatio()
```
optional double pb_cleanup_ratio = 47 [default = 0.5];

Returns:

The pbCleanupRatio.

hasMinimizeWithPropagationRestartPeriod

boolean hasMinimizeWithPropagationRestartPeriod()

 Parameters for an heuristic similar to the one described in "An effective
 learnt clause minimization approach for CDCL Sat Solvers",
 https://www.ijcai.org/proceedings/2017/0098.pdf

 For now, we have a somewhat simpler implementation where every x restart we
 spend y decisions on clause minimization. The minimization technique is the
 same as the one used to minimize core in max-sat. We also minimize problem
 clauses and not just the learned clause that we keep forever like in the
 paper.

 Changing these parameters or the kind of clause we minimize seems to have
 a big impact on the overall perf on our benchmarks. So this technique seems
 definitely useful, but it is hard to tune properly.

optional int32 minimize_with_propagation_restart_period = 96 [default = 10];

Returns:: Whether the minimizeWithPropagationRestartPeriod field is set.

getMinimizeWithPropagationRestartPeriod

int getMinimizeWithPropagationRestartPeriod()

 Parameters for an heuristic similar to the one described in "An effective
 learnt clause minimization approach for CDCL Sat Solvers",
 https://www.ijcai.org/proceedings/2017/0098.pdf

 For now, we have a somewhat simpler implementation where every x restart we
 spend y decisions on clause minimization. The minimization technique is the
 same as the one used to minimize core in max-sat. We also minimize problem
 clauses and not just the learned clause that we keep forever like in the
 paper.

 Changing these parameters or the kind of clause we minimize seems to have
 a big impact on the overall perf on our benchmarks. So this technique seems
 definitely useful, but it is hard to tune properly.

optional int32 minimize_with_propagation_restart_period = 96 [default = 10];

Returns:: The minimizeWithPropagationRestartPeriod.

hasMinimizeWithPropagationNumDecisions
```
boolean hasMinimizeWithPropagationNumDecisions()
```
optional int32 minimize_with_propagation_num_decisions = 97 [default = 1000];

Returns:

Whether the minimizeWithPropagationNumDecisions field is set.

getMinimizeWithPropagationNumDecisions
```
int getMinimizeWithPropagationNumDecisions()
```
optional int32 minimize_with_propagation_num_decisions = 97 [default = 1000];

Returns:

The minimizeWithPropagationNumDecisions.

hasVariableActivityDecay

boolean hasVariableActivityDecay()

 Each time a conflict is found, the activities of some variables are
 increased by one. Then, the activity of all variables are multiplied by
 variable_activity_decay.

 To implement this efficiently, the activity of all the variables is not
 decayed at each conflict. Instead, the activity increment is multiplied by
 1 / decay. When an activity reach max_variable_activity_value, all the
 activity are multiplied by 1 / max_variable_activity_value.

optional double variable_activity_decay = 15 [default = 0.8];

Returns:: Whether the variableActivityDecay field is set.

getVariableActivityDecay

double getVariableActivityDecay()

 Each time a conflict is found, the activities of some variables are
 increased by one. Then, the activity of all variables are multiplied by
 variable_activity_decay.

 To implement this efficiently, the activity of all the variables is not
 decayed at each conflict. Instead, the activity increment is multiplied by
 1 / decay. When an activity reach max_variable_activity_value, all the
 activity are multiplied by 1 / max_variable_activity_value.

optional double variable_activity_decay = 15 [default = 0.8];

Returns:: The variableActivityDecay.

hasMaxVariableActivityValue
```
boolean hasMaxVariableActivityValue()
```
optional double max_variable_activity_value = 16 [default = 1e+100];

Returns:

Whether the maxVariableActivityValue field is set.

getMaxVariableActivityValue
```
double getMaxVariableActivityValue()
```
optional double max_variable_activity_value = 16 [default = 1e+100];

Returns:

The maxVariableActivityValue.

hasGlucoseMaxDecay

boolean hasGlucoseMaxDecay()

 The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until
 0.95. This "hack" seems to work well and comes from:

 Glucose 2.3 in the SAT 2013 Competition - SAT Competition 2013
 http://edacc4.informatik.uni-ulm.de/SC13/solver-description-download/136

optional double glucose_max_decay = 22 [default = 0.95];

Returns:: Whether the glucoseMaxDecay field is set.

getGlucoseMaxDecay

double getGlucoseMaxDecay()

 The activity starts at 0.8 and increment by 0.01 every 5000 conflicts until
 0.95. This "hack" seems to work well and comes from:

 Glucose 2.3 in the SAT 2013 Competition - SAT Competition 2013
 http://edacc4.informatik.uni-ulm.de/SC13/solver-description-download/136

optional double glucose_max_decay = 22 [default = 0.95];

Returns:: The glucoseMaxDecay.

hasGlucoseDecayIncrement
```
boolean hasGlucoseDecayIncrement()
```
optional double glucose_decay_increment = 23 [default = 0.01];

Returns:

Whether the glucoseDecayIncrement field is set.

getGlucoseDecayIncrement
```
double getGlucoseDecayIncrement()
```
optional double glucose_decay_increment = 23 [default = 0.01];

Returns:

The glucoseDecayIncrement.

hasGlucoseDecayIncrementPeriod
```
boolean hasGlucoseDecayIncrementPeriod()
```
optional int32 glucose_decay_increment_period = 24 [default = 5000];

Returns:

Whether the glucoseDecayIncrementPeriod field is set.

getGlucoseDecayIncrementPeriod
```
int getGlucoseDecayIncrementPeriod()
```
optional int32 glucose_decay_increment_period = 24 [default = 5000];

Returns:

The glucoseDecayIncrementPeriod.

hasClauseActivityDecay
```
boolean hasClauseActivityDecay()
```
```
 Clause activity parameters (same effect as the one on the variables).
 
```
optional double clause_activity_decay = 17 [default = 0.999];
Returns:

Whether the clauseActivityDecay field is set.

getClauseActivityDecay
```
double getClauseActivityDecay()
```
```
 Clause activity parameters (same effect as the one on the variables).
 
```
optional double clause_activity_decay = 17 [default = 0.999];
Returns:

The clauseActivityDecay.

hasMaxClauseActivityValue
```
boolean hasMaxClauseActivityValue()
```
optional double max_clause_activity_value = 18 [default = 1e+20];

Returns:

Whether the maxClauseActivityValue field is set.

getMaxClauseActivityValue
```
double getMaxClauseActivityValue()
```
optional double max_clause_activity_value = 18 [default = 1e+20];

Returns:

The maxClauseActivityValue.

getRestartAlgorithmsList

java.util.List<SatParameters.RestartAlgorithm> getRestartAlgorithmsList()

 The restart strategies will change each time the strategy_counter is
 increased. The current strategy will simply be the one at index
 strategy_counter modulo the number of strategy. Note that if this list
 includes a NO_RESTART, nothing will change when it is reached because the
 strategy_counter will only increment after a restart.

 The idea of switching of search strategy tailored for SAT/UNSAT comes from
 Chanseok Oh with his COMiniSatPS solver, see http://cs.nyu.edu/~chanseok/.
 But more generally, it seems REALLY beneficial to try different strategy.

repeated .operations_research.sat.SatParameters.RestartAlgorithm restart_algorithms = 61;

Returns:: A list containing the restartAlgorithms.

getRestartAlgorithmsCount

int getRestartAlgorithmsCount()

 The restart strategies will change each time the strategy_counter is
 increased. The current strategy will simply be the one at index
 strategy_counter modulo the number of strategy. Note that if this list
 includes a NO_RESTART, nothing will change when it is reached because the
 strategy_counter will only increment after a restart.

 The idea of switching of search strategy tailored for SAT/UNSAT comes from
 Chanseok Oh with his COMiniSatPS solver, see http://cs.nyu.edu/~chanseok/.
 But more generally, it seems REALLY beneficial to try different strategy.

repeated .operations_research.sat.SatParameters.RestartAlgorithm restart_algorithms = 61;

Returns:: The count of restartAlgorithms.

getRestartAlgorithms

SatParameters.RestartAlgorithm getRestartAlgorithms(int index)

 The restart strategies will change each time the strategy_counter is
 increased. The current strategy will simply be the one at index
 strategy_counter modulo the number of strategy. Note that if this list
 includes a NO_RESTART, nothing will change when it is reached because the
 strategy_counter will only increment after a restart.

 The idea of switching of search strategy tailored for SAT/UNSAT comes from
 Chanseok Oh with his COMiniSatPS solver, see http://cs.nyu.edu/~chanseok/.
 But more generally, it seems REALLY beneficial to try different strategy.

repeated .operations_research.sat.SatParameters.RestartAlgorithm restart_algorithms = 61;

Parameters:: index - The index of the element to return.
Returns:: The restartAlgorithms at the given index.

hasDefaultRestartAlgorithms
```
boolean hasDefaultRestartAlgorithms()
```
optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];

Returns:

Whether the defaultRestartAlgorithms field is set.

getDefaultRestartAlgorithms
```
java.lang.String getDefaultRestartAlgorithms()
```
optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];

Returns:

The defaultRestartAlgorithms.

getDefaultRestartAlgorithmsBytes
```
com.google.protobuf.ByteString getDefaultRestartAlgorithmsBytes()
```
optional string default_restart_algorithms = 70 [default = "LUBY_RESTART,LBD_MOVING_AVERAGE_RESTART,DL_MOVING_AVERAGE_RESTART"];

Returns:

The bytes for defaultRestartAlgorithms.

hasRestartPeriod

boolean hasRestartPeriod()

 Restart period for the FIXED_RESTART strategy. This is also the multiplier
 used by the LUBY_RESTART strategy.

optional int32 restart_period = 30 [default = 50];

Returns:: Whether the restartPeriod field is set.

getRestartPeriod

int getRestartPeriod()

 Restart period for the FIXED_RESTART strategy. This is also the multiplier
 used by the LUBY_RESTART strategy.

optional int32 restart_period = 30 [default = 50];

Returns:: The restartPeriod.

hasRestartRunningWindowSize
```
boolean hasRestartRunningWindowSize()
```
```
 Size of the window for the moving average restarts.
 
```
optional int32 restart_running_window_size = 62 [default = 50];
Returns:

Whether the restartRunningWindowSize field is set.

getRestartRunningWindowSize
```
int getRestartRunningWindowSize()
```
```
 Size of the window for the moving average restarts.
 
```
optional int32 restart_running_window_size = 62 [default = 50];
Returns:

The restartRunningWindowSize.

hasRestartDlAverageRatio

boolean hasRestartDlAverageRatio()

 In the moving average restart algorithms, a restart is triggered if the
 window average times this ratio is greater that the global average.

optional double restart_dl_average_ratio = 63 [default = 1];

Returns:: Whether the restartDlAverageRatio field is set.

getRestartDlAverageRatio

double getRestartDlAverageRatio()

 In the moving average restart algorithms, a restart is triggered if the
 window average times this ratio is greater that the global average.

optional double restart_dl_average_ratio = 63 [default = 1];

Returns:: The restartDlAverageRatio.

hasRestartLbdAverageRatio
```
boolean hasRestartLbdAverageRatio()
```
optional double restart_lbd_average_ratio = 71 [default = 1];

Returns:

Whether the restartLbdAverageRatio field is set.

getRestartLbdAverageRatio
```
double getRestartLbdAverageRatio()
```
optional double restart_lbd_average_ratio = 71 [default = 1];

Returns:

The restartLbdAverageRatio.

hasUseBlockingRestart

boolean hasUseBlockingRestart()

 Block a moving restart algorithm if the trail size of the current conflict
 is greater than the multiplier times the moving average of the trail size
 at the previous conflicts.

optional bool use_blocking_restart = 64 [default = false];

Returns:: Whether the useBlockingRestart field is set.

getUseBlockingRestart

boolean getUseBlockingRestart()

 Block a moving restart algorithm if the trail size of the current conflict
 is greater than the multiplier times the moving average of the trail size
 at the previous conflicts.

optional bool use_blocking_restart = 64 [default = false];

Returns:: The useBlockingRestart.

hasBlockingRestartWindowSize
```
boolean hasBlockingRestartWindowSize()
```
optional int32 blocking_restart_window_size = 65 [default = 5000];

Returns:

Whether the blockingRestartWindowSize field is set.

getBlockingRestartWindowSize
```
int getBlockingRestartWindowSize()
```
optional int32 blocking_restart_window_size = 65 [default = 5000];

Returns:

The blockingRestartWindowSize.

hasBlockingRestartMultiplier
```
boolean hasBlockingRestartMultiplier()
```
optional double blocking_restart_multiplier = 66 [default = 1.4];

Returns:

Whether the blockingRestartMultiplier field is set.

getBlockingRestartMultiplier
```
double getBlockingRestartMultiplier()
```
optional double blocking_restart_multiplier = 66 [default = 1.4];

Returns:

The blockingRestartMultiplier.

hasNumConflictsBeforeStrategyChanges

boolean hasNumConflictsBeforeStrategyChanges()

 After each restart, if the number of conflict since the last strategy
 change is greater that this, then we increment a "strategy_counter" that
 can be use to change the search strategy used by the following restarts.

optional int32 num_conflicts_before_strategy_changes = 68 [default = 0];

Returns:: Whether the numConflictsBeforeStrategyChanges field is set.

getNumConflictsBeforeStrategyChanges

int getNumConflictsBeforeStrategyChanges()

 After each restart, if the number of conflict since the last strategy
 change is greater that this, then we increment a "strategy_counter" that
 can be use to change the search strategy used by the following restarts.

optional int32 num_conflicts_before_strategy_changes = 68 [default = 0];

Returns:: The numConflictsBeforeStrategyChanges.

hasStrategyChangeIncreaseRatio
```
boolean hasStrategyChangeIncreaseRatio()
```
```
 The parameter num_conflicts_before_strategy_changes is increased by that
 much after each strategy change.
 
```
optional double strategy_change_increase_ratio = 69 [default = 0];
Returns:

Whether the strategyChangeIncreaseRatio field is set.

getStrategyChangeIncreaseRatio

double getStrategyChangeIncreaseRatio()

 The parameter num_conflicts_before_strategy_changes is increased by that
 much after each strategy change.

optional double strategy_change_increase_ratio = 69 [default = 0];

Returns:: The strategyChangeIncreaseRatio.

hasMaxTimeInSeconds

boolean hasMaxTimeInSeconds()

 Maximum time allowed in seconds to solve a problem.
 The counter will starts at the beginning of the Solve() call.

optional double max_time_in_seconds = 36 [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 at the beginning of the Solve() call.

optional double max_time_in_seconds = 36 [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 as close as possible to a second.

optional double max_deterministic_time = 67 [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 as close as possible to a second.

optional double max_deterministic_time = 67 [default = inf];

Returns:: The maxDeterministicTime.

hasMaxNumberOfConflicts

boolean hasMaxNumberOfConflicts()

 Maximum number of conflicts allowed to solve a problem.

 TODO(user): Maybe change the way the conflict limit is enforced?
 currently it is enforced on each independent internal SAT solve, rather
 than on the overall number of conflicts across all solves. So in the
 context of an optimization problem, this is not really usable directly by a
 client.

optional int64 max_number_of_conflicts = 37 [default = 9223372036854775807];

Returns:: Whether the maxNumberOfConflicts field is set.

getMaxNumberOfConflicts

long getMaxNumberOfConflicts()

 Maximum number of conflicts allowed to solve a problem.

 TODO(user): Maybe change the way the conflict limit is enforced?
 currently it is enforced on each independent internal SAT solve, rather
 than on the overall number of conflicts across all solves. So in the
 context of an optimization problem, this is not really usable directly by a
 client.

optional int64 max_number_of_conflicts = 37 [default = 9223372036854775807];

Returns:: The maxNumberOfConflicts.

hasMaxMemoryInMb

boolean hasMaxMemoryInMb()

 Maximum memory allowed for the whole thread containing the solver. The
 solver will abort as soon as it detects that this limit is crossed. As a
 result, this limit is approximative, but usually the solver will not go too
 much over.

 TODO(user): This is only used by the pure SAT solver, generalize to CP-SAT.

optional int64 max_memory_in_mb = 40 [default = 10000];

Returns:: Whether the maxMemoryInMb field is set.

getMaxMemoryInMb

long getMaxMemoryInMb()

 Maximum memory allowed for the whole thread containing the solver. The
 solver will abort as soon as it detects that this limit is crossed. As a
 result, this limit is approximative, but usually the solver will not go too
 much over.

 TODO(user): This is only used by the pure SAT solver, generalize to CP-SAT.

optional int64 max_memory_in_mb = 40 [default = 10000];

Returns:: The maxMemoryInMb.

hasAbsoluteGapLimit

boolean hasAbsoluteGapLimit()

 Stop the search when the gap between the best feasible objective (O) and
 our best objective bound (B) is smaller than a limit.
 The exact definition is:
 - Absolute: abs(O - B)
 - Relative: abs(O - B) / max(1, abs(O)).

 Important: The relative gap depends on the objective offset! If you
 artificially shift the objective, you will get widely different value of
 the relative gap.

 Note that if the gap is reached, the search status will be OPTIMAL. But
 one can check the best objective bound to see the actual gap.

 If the objective is integer, then any absolute gap < 1 will lead to a true
 optimal. If the objective is floating point, a gap of zero make little
 sense so is is why we use a non-zero default value. At the end of the
 search, we will display a warning if OPTIMAL is reported yet the gap is
 greater than this absolute gap.

optional double absolute_gap_limit = 159 [default = 0.0001];

Returns:: Whether the absoluteGapLimit field is set.

getAbsoluteGapLimit

double getAbsoluteGapLimit()

 Stop the search when the gap between the best feasible objective (O) and
 our best objective bound (B) is smaller than a limit.
 The exact definition is:
 - Absolute: abs(O - B)
 - Relative: abs(O - B) / max(1, abs(O)).

 Important: The relative gap depends on the objective offset! If you
 artificially shift the objective, you will get widely different value of
 the relative gap.

 Note that if the gap is reached, the search status will be OPTIMAL. But
 one can check the best objective bound to see the actual gap.

 If the objective is integer, then any absolute gap < 1 will lead to a true
 optimal. If the objective is floating point, a gap of zero make little
 sense so is is why we use a non-zero default value. At the end of the
 search, we will display a warning if OPTIMAL is reported yet the gap is
 greater than this absolute gap.

optional double absolute_gap_limit = 159 [default = 0.0001];

Returns:: The absoluteGapLimit.

hasRelativeGapLimit
```
boolean hasRelativeGapLimit()
```
optional double relative_gap_limit = 160 [default = 0];

Returns:

Whether the relativeGapLimit field is set.

getRelativeGapLimit
```
double getRelativeGapLimit()
```
optional double relative_gap_limit = 160 [default = 0];

Returns:

The relativeGapLimit.

hasRandomSeed

boolean hasRandomSeed()

 At the beginning of each solve, the random number generator used in some
 part of the solver is reinitialized to this seed. If you change the random
 seed, the solver may make different choices during the solving process.

 For some problems, the running time may vary a lot depending on small
 change in the solving algorithm. Running the solver with different seeds
 enables to have more robust benchmarks when evaluating new features.

optional int32 random_seed = 31 [default = 1];

Returns:: Whether the randomSeed field is set.

getRandomSeed

int getRandomSeed()

 At the beginning of each solve, the random number generator used in some
 part of the solver is reinitialized to this seed. If you change the random
 seed, the solver may make different choices during the solving process.

 For some problems, the running time may vary a lot depending on small
 change in the solving algorithm. Running the solver with different seeds
 enables to have more robust benchmarks when evaluating new features.

optional int32 random_seed = 31 [default = 1];

Returns:: The randomSeed.

hasPermuteVariableRandomly

boolean hasPermuteVariableRandomly()

 This is mainly here to test the solver variability. Note that in tests, if
 not explicitly set to false, all 3 options will be set to true so that
 clients do not rely on the solver returning a specific solution if they are
 many equivalent optimal solutions.

optional bool permute_variable_randomly = 178 [default = false];

Returns:: Whether the permuteVariableRandomly field is set.

getPermuteVariableRandomly

boolean getPermuteVariableRandomly()

 This is mainly here to test the solver variability. Note that in tests, if
 not explicitly set to false, all 3 options will be set to true so that
 clients do not rely on the solver returning a specific solution if they are
 many equivalent optimal solutions.

optional bool permute_variable_randomly = 178 [default = false];

Returns:: The permuteVariableRandomly.

hasPermutePresolveConstraintOrder
```
boolean hasPermutePresolveConstraintOrder()
```
optional bool permute_presolve_constraint_order = 179 [default = false];

Returns:

Whether the permutePresolveConstraintOrder field is set.

getPermutePresolveConstraintOrder
```
boolean getPermutePresolveConstraintOrder()
```
optional bool permute_presolve_constraint_order = 179 [default = false];

Returns:

The permutePresolveConstraintOrder.

hasUseAbslRandom
```
boolean hasUseAbslRandom()
```
optional bool use_absl_random = 180 [default = false];

Returns:

Whether the useAbslRandom field is set.

getUseAbslRandom
```
boolean getUseAbslRandom()
```
optional bool use_absl_random = 180 [default = false];

Returns:

The useAbslRandom.

hasLogSearchProgress

boolean hasLogSearchProgress()

 Whether the solver should log the search progress. This is the maing
 logging parameter and if this is false, none of the logging (callbacks,
 log_to_stdout, log_to_response, ...) will do anything.

optional bool log_search_progress = 41 [default = false];

Returns:: Whether the logSearchProgress field is set.

getLogSearchProgress

boolean getLogSearchProgress()

 Whether the solver should log the search progress. This is the maing
 logging parameter and if this is false, none of the logging (callbacks,
 log_to_stdout, log_to_response, ...) will do anything.

optional bool log_search_progress = 41 [default = false];

Returns:: The logSearchProgress.

hasLogSubsolverStatistics

boolean hasLogSubsolverStatistics()

 Whether the solver should display per sub-solver search statistics.
 This is only useful is log_search_progress is set to true, and if the
 number of search workers is > 1. Note that in all case we display a bit
 of stats with one line per subsolver.

optional bool log_subsolver_statistics = 189 [default = false];

Returns:: Whether the logSubsolverStatistics field is set.

getLogSubsolverStatistics

boolean getLogSubsolverStatistics()

 Whether the solver should display per sub-solver search statistics.
 This is only useful is log_search_progress is set to true, and if the
 number of search workers is > 1. Note that in all case we display a bit
 of stats with one line per subsolver.

optional bool log_subsolver_statistics = 189 [default = false];

Returns:: The logSubsolverStatistics.

hasLogPrefix
```
boolean hasLogPrefix()
```
```
 Add a prefix to all logs.
 
```
optional string log_prefix = 185 [default = ""];
Returns:

Whether the logPrefix field is set.

getLogPrefix
```
java.lang.String getLogPrefix()
```
```
 Add a prefix to all logs.
 
```
optional string log_prefix = 185 [default = ""];
Returns:

The logPrefix.

getLogPrefixBytes
```
com.google.protobuf.ByteString getLogPrefixBytes()
```
```
 Add a prefix to all logs.
 
```
optional string log_prefix = 185 [default = ""];
Returns:

The bytes for logPrefix.

hasLogToStdout
```
boolean hasLogToStdout()
```
```
 Log to stdout.
 
```
optional bool log_to_stdout = 186 [default = true];
Returns:

Whether the logToStdout field is set.

getLogToStdout
```
boolean getLogToStdout()
```
```
 Log to stdout.
 
```
optional bool log_to_stdout = 186 [default = true];
Returns:

The logToStdout.

hasLogToResponse
```
boolean hasLogToResponse()
```
```
 Log to response proto.
 
```
optional bool log_to_response = 187 [default = false];
Returns:

Whether the logToResponse field is set.

getLogToResponse
```
boolean getLogToResponse()
```
```
 Log to response proto.
 
```
optional bool log_to_response = 187 [default = false];
Returns:

The logToResponse.

hasUsePbResolution

boolean hasUsePbResolution()

 Whether to use pseudo-Boolean resolution to analyze a conflict. Note that
 this option only make sense if your problem is modelized using
 pseudo-Boolean constraints. If you only have clauses, this shouldn't change
 anything (except slow the solver down).

optional bool use_pb_resolution = 43 [default = false];

Returns:: Whether the usePbResolution field is set.

getUsePbResolution

boolean getUsePbResolution()

 Whether to use pseudo-Boolean resolution to analyze a conflict. Note that
 this option only make sense if your problem is modelized using
 pseudo-Boolean constraints. If you only have clauses, this shouldn't change
 anything (except slow the solver down).

optional bool use_pb_resolution = 43 [default = false];

Returns:: The usePbResolution.

hasMinimizeReductionDuringPbResolution

boolean hasMinimizeReductionDuringPbResolution()

 A different algorithm during PB resolution. It minimizes the number of
 calls to ReduceCoefficients() which can be time consuming. However, the
 search space will be different and if the coefficients are large, this may
 lead to integer overflows that could otherwise be prevented.

optional bool minimize_reduction_during_pb_resolution = 48 [default = false];

Returns:: Whether the minimizeReductionDuringPbResolution field is set.

getMinimizeReductionDuringPbResolution

boolean getMinimizeReductionDuringPbResolution()

 A different algorithm during PB resolution. It minimizes the number of
 calls to ReduceCoefficients() which can be time consuming. However, the
 search space will be different and if the coefficients are large, this may
 lead to integer overflows that could otherwise be prevented.

optional bool minimize_reduction_during_pb_resolution = 48 [default = false];

Returns:: The minimizeReductionDuringPbResolution.

hasCountAssumptionLevelsInLbd

boolean hasCountAssumptionLevelsInLbd()

 Whether or not the assumption levels are taken into account during the LBD
 computation. According to the reference below, not counting them improves
 the solver in some situation. Note that this only impact solves under
 assumptions.

 Gilles Audemard, Jean-Marie Lagniez, Laurent Simon, "Improving Glucose for
 Incremental SAT Solving with Assumptions: Application to MUS Extraction"
 Theory and Applications of Satisfiability Testing - SAT 2013, Lecture Notes
 in Computer Science Volume 7962, 2013, pp 309-317.

optional bool count_assumption_levels_in_lbd = 49 [default = true];

Returns:: Whether the countAssumptionLevelsInLbd field is set.

getCountAssumptionLevelsInLbd

boolean getCountAssumptionLevelsInLbd()

 Whether or not the assumption levels are taken into account during the LBD
 computation. According to the reference below, not counting them improves
 the solver in some situation. Note that this only impact solves under
 assumptions.

 Gilles Audemard, Jean-Marie Lagniez, Laurent Simon, "Improving Glucose for
 Incremental SAT Solving with Assumptions: Application to MUS Extraction"
 Theory and Applications of Satisfiability Testing - SAT 2013, Lecture Notes
 in Computer Science Volume 7962, 2013, pp 309-317.

optional bool count_assumption_levels_in_lbd = 49 [default = true];

Returns:: The countAssumptionLevelsInLbd.

hasPresolveBveThreshold

boolean hasPresolveBveThreshold()

 During presolve, only try to perform the bounded variable elimination (BVE)
 of a variable x if the number of occurrences of x times the number of
 occurrences of not(x) is not greater than this parameter.

optional int32 presolve_bve_threshold = 54 [default = 500];

Returns:: Whether the presolveBveThreshold field is set.

getPresolveBveThreshold

int getPresolveBveThreshold()

 During presolve, only try to perform the bounded variable elimination (BVE)
 of a variable x if the number of occurrences of x times the number of
 occurrences of not(x) is not greater than this parameter.

optional int32 presolve_bve_threshold = 54 [default = 500];

Returns:: The presolveBveThreshold.

hasPresolveBveClauseWeight

boolean hasPresolveBveClauseWeight()

 During presolve, we apply BVE only if this weight times the number of
 clauses plus the number of clause literals is not increased.

optional int32 presolve_bve_clause_weight = 55 [default = 3];

Returns:: Whether the presolveBveClauseWeight field is set.

getPresolveBveClauseWeight

int getPresolveBveClauseWeight()

 During presolve, we apply BVE only if this weight times the number of
 clauses plus the number of clause literals is not increased.

optional int32 presolve_bve_clause_weight = 55 [default = 3];

Returns:: The presolveBveClauseWeight.

hasProbingDeterministicTimeLimit

boolean hasProbingDeterministicTimeLimit()

 The maximum "deterministic" time limit to spend in probing. A value of
 zero will disable the probing.

 TODO(user): Clean up. The first one is used in CP-SAT, the other in pure
 SAT presolve.

optional double probing_deterministic_time_limit = 226 [default = 1];

Returns:: Whether the probingDeterministicTimeLimit field is set.

getProbingDeterministicTimeLimit

double getProbingDeterministicTimeLimit()

 The maximum "deterministic" time limit to spend in probing. A value of
 zero will disable the probing.

 TODO(user): Clean up. The first one is used in CP-SAT, the other in pure
 SAT presolve.

optional double probing_deterministic_time_limit = 226 [default = 1];

Returns:: The probingDeterministicTimeLimit.

hasPresolveProbingDeterministicTimeLimit
```
boolean hasPresolveProbingDeterministicTimeLimit()
```
optional double presolve_probing_deterministic_time_limit = 57 [default = 30];

Returns:

Whether the presolveProbingDeterministicTimeLimit field is set.

getPresolveProbingDeterministicTimeLimit
```
double getPresolveProbingDeterministicTimeLimit()
```
optional double presolve_probing_deterministic_time_limit = 57 [default = 30];

Returns:

The presolveProbingDeterministicTimeLimit.

hasPresolveBlockedClause
```
boolean hasPresolveBlockedClause()
```
```
 Whether we use an heuristic to detect some basic case of blocked clause
 in the SAT presolve.
 
```
optional bool presolve_blocked_clause = 88 [default = true];
Returns:

Whether the presolveBlockedClause field is set.

getPresolveBlockedClause

boolean getPresolveBlockedClause()

 Whether we use an heuristic to detect some basic case of blocked clause
 in the SAT presolve.

optional bool presolve_blocked_clause = 88 [default = true];

Returns:: The presolveBlockedClause.

hasPresolveUseBva
```
boolean hasPresolveUseBva()
```
```
 Whether or not we use Bounded Variable Addition (BVA) in the presolve.
 
```
optional bool presolve_use_bva = 72 [default = true];
Returns:

Whether the presolveUseBva field is set.

getPresolveUseBva

boolean getPresolveUseBva()

 Whether or not we use Bounded Variable Addition (BVA) in the presolve.

optional bool presolve_use_bva = 72 [default = true];

Returns:: The presolveUseBva.

hasPresolveBvaThreshold

boolean hasPresolveBvaThreshold()

 Apply Bounded Variable Addition (BVA) if the number of clauses is reduced
 by stricly more than this threshold. The algorithm described in the paper
 uses 0, but quick experiments showed that 1 is a good value. It may not be
 worth it to add a new variable just to remove one clause.

optional int32 presolve_bva_threshold = 73 [default = 1];

Returns:: Whether the presolveBvaThreshold field is set.

getPresolveBvaThreshold

int getPresolveBvaThreshold()

 Apply Bounded Variable Addition (BVA) if the number of clauses is reduced
 by stricly more than this threshold. The algorithm described in the paper
 uses 0, but quick experiments showed that 1 is a good value. It may not be
 worth it to add a new variable just to remove one clause.

optional int32 presolve_bva_threshold = 73 [default = 1];

Returns:: The presolveBvaThreshold.

hasMaxPresolveIterations

boolean hasMaxPresolveIterations()

 In case of large reduction in a presolve iteration, we perform multiple
 presolve iterations. This parameter controls the maximum number of such
 presolve iterations.

optional int32 max_presolve_iterations = 138 [default = 3];

Returns:: Whether the maxPresolveIterations field is set.

getMaxPresolveIterations

int getMaxPresolveIterations()

 In case of large reduction in a presolve iteration, we perform multiple
 presolve iterations. This parameter controls the maximum number of such
 presolve iterations.

optional int32 max_presolve_iterations = 138 [default = 3];

Returns:: The maxPresolveIterations.

hasCpModelPresolve
```
boolean hasCpModelPresolve()
```
```
 Whether we presolve the cp_model before solving it.
 
```
optional bool cp_model_presolve = 86 [default = true];
Returns:

Whether the cpModelPresolve field is set.

getCpModelPresolve
```
boolean getCpModelPresolve()
```
```
 Whether we presolve the cp_model before solving it.
 
```
optional bool cp_model_presolve = 86 [default = true];
Returns:

The cpModelPresolve.

hasCpModelProbingLevel
```
boolean hasCpModelProbingLevel()
```
```
 How much effort do we spend on probing. 0 disables it completely.
 
```
optional int32 cp_model_probing_level = 110 [default = 2];
Returns:

Whether the cpModelProbingLevel field is set.

getCpModelProbingLevel
```
int getCpModelProbingLevel()
```
```
 How much effort do we spend on probing. 0 disables it completely.
 
```
optional int32 cp_model_probing_level = 110 [default = 2];
Returns:

The cpModelProbingLevel.

hasCpModelUseSatPresolve
```
boolean hasCpModelUseSatPresolve()
```
```
 Whether we also use the sat presolve when cp_model_presolve is true.
 
```
optional bool cp_model_use_sat_presolve = 93 [default = true];
Returns:

Whether the cpModelUseSatPresolve field is set.

getCpModelUseSatPresolve
```
boolean getCpModelUseSatPresolve()
```
```
 Whether we also use the sat presolve when cp_model_presolve is true.
 
```
optional bool cp_model_use_sat_presolve = 93 [default = true];
Returns:

The cpModelUseSatPresolve.

hasUseSatInprocessing
```
boolean hasUseSatInprocessing()
```
optional bool use_sat_inprocessing = 163 [default = false];

Returns:

Whether the useSatInprocessing field is set.

getUseSatInprocessing
```
boolean getUseSatInprocessing()
```
optional bool use_sat_inprocessing = 163 [default = false];

Returns:

The useSatInprocessing.

hasDetectTableWithCost

boolean hasDetectTableWithCost()

 If true, we detect variable that are unique to a table constraint and only
 there to encode a cost on each tuple. This is usually the case when a WCSP
 (weighted constraint program) is encoded into CP-SAT format.

 This can lead to a dramatic speed-up for such problems but is still
 experimental at this point.

optional bool detect_table_with_cost = 216 [default = false];

Returns:: Whether the detectTableWithCost field is set.

getDetectTableWithCost

boolean getDetectTableWithCost()

 If true, we detect variable that are unique to a table constraint and only
 there to encode a cost on each tuple. This is usually the case when a WCSP
 (weighted constraint program) is encoded into CP-SAT format.

 This can lead to a dramatic speed-up for such problems but is still
 experimental at this point.

optional bool detect_table_with_cost = 216 [default = false];

Returns:: The detectTableWithCost.

hasTableCompressionLevel

boolean hasTableCompressionLevel()

 How much we try to "compress" a table constraint. Compressing more leads to
 less Booleans and faster propagation but can reduced the quality of the lp
 relaxation. Values goes from 0 to 3 where we always try to fully compress a
 table. At 2, we try to automatically decide if it is worth it.

optional int32 table_compression_level = 217 [default = 2];

Returns:: Whether the tableCompressionLevel field is set.

getTableCompressionLevel

int getTableCompressionLevel()

 How much we try to "compress" a table constraint. Compressing more leads to
 less Booleans and faster propagation but can reduced the quality of the lp
 relaxation. Values goes from 0 to 3 where we always try to fully compress a
 table. At 2, we try to automatically decide if it is worth it.

optional int32 table_compression_level = 217 [default = 2];

Returns:: The tableCompressionLevel.

hasExpandAlldiffConstraints

boolean hasExpandAlldiffConstraints()

 If true, expand all_different constraints that are not permutations.
 Permutations (#Variables = #Values) are always expanded.

optional bool expand_alldiff_constraints = 170 [default = false];

Returns:: Whether the expandAlldiffConstraints field is set.

getExpandAlldiffConstraints

boolean getExpandAlldiffConstraints()

 If true, expand all_different constraints that are not permutations.
 Permutations (#Variables = #Values) are always expanded.

optional bool expand_alldiff_constraints = 170 [default = false];

Returns:: The expandAlldiffConstraints.

hasExpandReservoirConstraints

boolean hasExpandReservoirConstraints()

 If true, expand the reservoir constraints by creating booleans for all
 possible precedences between event and encoding the constraint.

optional bool expand_reservoir_constraints = 182 [default = true];

Returns:: Whether the expandReservoirConstraints field is set.

getExpandReservoirConstraints

boolean getExpandReservoirConstraints()

 If true, expand the reservoir constraints by creating booleans for all
 possible precedences between event and encoding the constraint.

optional bool expand_reservoir_constraints = 182 [default = true];

Returns:: The expandReservoirConstraints.

hasDisableConstraintExpansion

boolean hasDisableConstraintExpansion()

 If true, it disable all constraint expansion.
 This should only be used to test the presolve of expanded constraints.

optional bool disable_constraint_expansion = 181 [default = false];

Returns:: Whether the disableConstraintExpansion field is set.

getDisableConstraintExpansion

boolean getDisableConstraintExpansion()

 If true, it disable all constraint expansion.
 This should only be used to test the presolve of expanded constraints.

optional bool disable_constraint_expansion = 181 [default = false];

Returns:: The disableConstraintExpansion.

hasEncodeComplexLinearConstraintWithInteger
```
boolean hasEncodeComplexLinearConstraintWithInteger()
```
```
 Linear constraint with a complex right hand side (more than a single
 interval) need to be expanded, there is a couple of way to do that.
 
```
optional bool encode_complex_linear_constraint_with_integer = 223 [default = false];
Returns:

Whether the encodeComplexLinearConstraintWithInteger field is set.

getEncodeComplexLinearConstraintWithInteger

boolean getEncodeComplexLinearConstraintWithInteger()

 Linear constraint with a complex right hand side (more than a single
 interval) need to be expanded, there is a couple of way to do that.

optional bool encode_complex_linear_constraint_with_integer = 223 [default = false];

Returns:: The encodeComplexLinearConstraintWithInteger.

hasMergeNoOverlapWorkLimit

boolean hasMergeNoOverlapWorkLimit()

 During presolve, we use a maximum clique heuristic to merge together
 no-overlap constraints or at most one constraints. This code can be slow,
 so we have a limit in place on the number of explored nodes in the
 underlying graph. The internal limit is an int64, but we use double here to
 simplify manual input.

optional double merge_no_overlap_work_limit = 145 [default = 1000000000000];

Returns:: Whether the mergeNoOverlapWorkLimit field is set.

getMergeNoOverlapWorkLimit

double getMergeNoOverlapWorkLimit()

 During presolve, we use a maximum clique heuristic to merge together
 no-overlap constraints or at most one constraints. This code can be slow,
 so we have a limit in place on the number of explored nodes in the
 underlying graph. The internal limit is an int64, but we use double here to
 simplify manual input.

optional double merge_no_overlap_work_limit = 145 [default = 1000000000000];

Returns:: The mergeNoOverlapWorkLimit.

hasMergeAtMostOneWorkLimit
```
boolean hasMergeAtMostOneWorkLimit()
```
optional double merge_at_most_one_work_limit = 146 [default = 100000000];

Returns:

Whether the mergeAtMostOneWorkLimit field is set.

getMergeAtMostOneWorkLimit
```
double getMergeAtMostOneWorkLimit()
```
optional double merge_at_most_one_work_limit = 146 [default = 100000000];

Returns:

The mergeAtMostOneWorkLimit.

hasPresolveSubstitutionLevel

boolean hasPresolveSubstitutionLevel()

 How much substitution (also called free variable aggregation in MIP
 litterature) should we perform at presolve. This currently only concerns
 variable appearing only in linear constraints. For now the value 0 turns it
 off and any positive value performs substitution.

optional int32 presolve_substitution_level = 147 [default = 1];

Returns:: Whether the presolveSubstitutionLevel field is set.

getPresolveSubstitutionLevel

int getPresolveSubstitutionLevel()

 How much substitution (also called free variable aggregation in MIP
 litterature) should we perform at presolve. This currently only concerns
 variable appearing only in linear constraints. For now the value 0 turns it
 off and any positive value performs substitution.

optional int32 presolve_substitution_level = 147 [default = 1];

Returns:: The presolveSubstitutionLevel.

hasPresolveExtractIntegerEnforcement

boolean hasPresolveExtractIntegerEnforcement()

 If true, we will extract from linear constraints, enforcement literals of
 the form "integer variable at bound => simplified constraint". This should
 always be beneficial except that we don't always handle them as efficiently
 as we could for now. This causes problem on manna81.mps (LP relaxation not
 as tight it seems) and on neos-3354841-apure.mps.gz (too many literals
 created this way).

optional bool presolve_extract_integer_enforcement = 174 [default = false];

Returns:: Whether the presolveExtractIntegerEnforcement field is set.

getPresolveExtractIntegerEnforcement

boolean getPresolveExtractIntegerEnforcement()

 If true, we will extract from linear constraints, enforcement literals of
 the form "integer variable at bound => simplified constraint". This should
 always be beneficial except that we don't always handle them as efficiently
 as we could for now. This causes problem on manna81.mps (LP relaxation not
 as tight it seems) and on neos-3354841-apure.mps.gz (too many literals
 created this way).

optional bool presolve_extract_integer_enforcement = 174 [default = false];

Returns:: The presolveExtractIntegerEnforcement.

hasPresolveInclusionWorkLimit

boolean hasPresolveInclusionWorkLimit()

 A few presolve operations involve detecting constraints included in other
 constraint. Since there can be a quadratic number of such pairs, and
 processing them usually involve scanning them, the complexity of these
 operations can be big. This enforce a local deterministic limit on the
 number of entries scanned. Default is 1e8.

 A value of zero will disable these presolve rules completely.

optional int64 presolve_inclusion_work_limit = 201 [default = 100000000];

Returns:: Whether the presolveInclusionWorkLimit field is set.

getPresolveInclusionWorkLimit

long getPresolveInclusionWorkLimit()

 A few presolve operations involve detecting constraints included in other
 constraint. Since there can be a quadratic number of such pairs, and
 processing them usually involve scanning them, the complexity of these
 operations can be big. This enforce a local deterministic limit on the
 number of entries scanned. Default is 1e8.

 A value of zero will disable these presolve rules completely.

optional int64 presolve_inclusion_work_limit = 201 [default = 100000000];

Returns:: The presolveInclusionWorkLimit.

hasIgnoreNames
```
boolean hasIgnoreNames()
```
```
 If true, we don't keep names in our internal copy of the user given model.
 
```
optional bool ignore_names = 202 [default = true];
Returns:

Whether the ignoreNames field is set.

getIgnoreNames

boolean getIgnoreNames()

 If true, we don't keep names in our internal copy of the user given model.

optional bool ignore_names = 202 [default = true];

Returns:: The ignoreNames.

hasInferAllDiffs

boolean hasInferAllDiffs()

 Run a max-clique code amongst all the x != y we can find and try to infer
 set of variables that are all different. This allows to close neos16.mps
 for instance. Note that we only run this code if there is no all_diff
 already in the model so that if a user want to add some all_diff, we assume
 it is well done and do not try to add more.

optional bool infer_all_diffs = 233 [default = true];

Returns:: Whether the inferAllDiffs field is set.

getInferAllDiffs

boolean getInferAllDiffs()

 Run a max-clique code amongst all the x != y we can find and try to infer
 set of variables that are all different. This allows to close neos16.mps
 for instance. Note that we only run this code if there is no all_diff
 already in the model so that if a user want to add some all_diff, we assume
 it is well done and do not try to add more.

optional bool infer_all_diffs = 233 [default = true];

Returns:: The inferAllDiffs.

hasFindBigLinearOverlap

boolean hasFindBigLinearOverlap()

 Try to find large "rectangle" in the linear constraint matrix with
 identical lines. If such rectangle is big enough, we can introduce a new
 integer variable corresponding to the common expression and greatly reduce
 the number of non-zero.

optional bool find_big_linear_overlap = 234 [default = true];

Returns:: Whether the findBigLinearOverlap field is set.

getFindBigLinearOverlap

boolean getFindBigLinearOverlap()

 Try to find large "rectangle" in the linear constraint matrix with
 identical lines. If such rectangle is big enough, we can introduce a new
 integer variable corresponding to the common expression and greatly reduce
 the number of non-zero.

optional bool find_big_linear_overlap = 234 [default = true];

Returns:: The findBigLinearOverlap.

hasNumWorkers

boolean hasNumWorkers()

 Specify the number of parallel workers (i.e. threads) to use during search.
 This should usually be lower than your number of available cpus +
 hyperthread in your machine.

 A value of 0 means the solver will try to use all cores on the machine.
 A number of 1 means no parallelism.

 Note that 'num_workers' is the preferred name, but if it is set to zero,
 we will still read the deprecated 'num_search_worker'.

 As of 2020-04-10, if you're using SAT via MPSolver (to solve integer
 programs) this field is overridden with a value of 8, if the field is not
 set *explicitly*. Thus, always set this field explicitly or via
 MPSolver::SetNumThreads().

optional int32 num_workers = 206 [default = 0];

Returns:: Whether the numWorkers field is set.

getNumWorkers

int getNumWorkers()

 Specify the number of parallel workers (i.e. threads) to use during search.
 This should usually be lower than your number of available cpus +
 hyperthread in your machine.

 A value of 0 means the solver will try to use all cores on the machine.
 A number of 1 means no parallelism.

 Note that 'num_workers' is the preferred name, but if it is set to zero,
 we will still read the deprecated 'num_search_worker'.

 As of 2020-04-10, if you're using SAT via MPSolver (to solve integer
 programs) this field is overridden with a value of 8, if the field is not
 set *explicitly*. Thus, always set this field explicitly or via
 MPSolver::SetNumThreads().

optional int32 num_workers = 206 [default = 0];

Returns:: The numWorkers.

hasNumSearchWorkers
```
boolean hasNumSearchWorkers()
```
optional int32 num_search_workers = 100 [default = 0];

Returns:

Whether the numSearchWorkers field is set.

getNumSearchWorkers
```
int getNumSearchWorkers()
```
optional int32 num_search_workers = 100 [default = 0];

Returns:

The numSearchWorkers.

hasMinNumLnsWorkers
```
boolean hasMinNumLnsWorkers()
```
```
 Obsolete parameter. No-op.
 
```
optional int32 min_num_lns_workers = 211 [default = 2];
Returns:

Whether the minNumLnsWorkers field is set.

getMinNumLnsWorkers
```
int getMinNumLnsWorkers()
```
```
 Obsolete parameter. No-op.
 
```
optional int32 min_num_lns_workers = 211 [default = 2];
Returns:

The minNumLnsWorkers.

getSubsolversList

java.util.List<java.lang.String> getSubsolversList()

 In multi-thread, the solver can be mainly seen as a portfolio of solvers
 with different parameters. This field indicates the names of the parameters
 that are used in multithread.

 See cp_model_search.cc to see a list of the names and the default value (if
 left empty) that looks like:
 - default_lp           (linearization_level:1)
 - fixed                (only if fixed search specified or scheduling)
 - no_lp                (linearization_level:0)
 - max_lp               (linearization_level:2)
 - pseudo_costs         (only if objective, change search heuristic)
 - reduced_costs        (only if objective, change search heuristic)
 - quick_restart        (kind of probing)
 - quick_restart_no_lp  (kind of probing with linearization_level:0)
 - lb_tree_search       (to improve lower bound, MIP like tree search)
 - probing              (continuous probing and shaving)

 Also, note that some set of parameters will be ignored if they do not make
 sense. For instance if there is no objective, pseudo_cost or reduced_cost
 search will be ignored. Core based search will only work if the objective
 has many terms. If there is no fixed strategy fixed will be ignored. And so
 on.

 The order is important, as only the first usable "num_workers -
 min_num_lns_workers" subsolvers will be scheduled. You can see in the log
 which one are selected for a given run. All the others will be LNS if there
 is an objective, or randomized SAT search for pure satisfiability problems.

repeated string subsolvers = 207;

Returns:: A list containing the subsolvers.

getSubsolversCount

int getSubsolversCount()

 In multi-thread, the solver can be mainly seen as a portfolio of solvers
 with different parameters. This field indicates the names of the parameters
 that are used in multithread.

 See cp_model_search.cc to see a list of the names and the default value (if
 left empty) that looks like:
 - default_lp           (linearization_level:1)
 - fixed                (only if fixed search specified or scheduling)
 - no_lp                (linearization_level:0)
 - max_lp               (linearization_level:2)
 - pseudo_costs         (only if objective, change search heuristic)
 - reduced_costs        (only if objective, change search heuristic)
 - quick_restart        (kind of probing)
 - quick_restart_no_lp  (kind of probing with linearization_level:0)
 - lb_tree_search       (to improve lower bound, MIP like tree search)
 - probing              (continuous probing and shaving)

 Also, note that some set of parameters will be ignored if they do not make
 sense. For instance if there is no objective, pseudo_cost or reduced_cost
 search will be ignored. Core based search will only work if the objective
 has many terms. If there is no fixed strategy fixed will be ignored. And so
 on.

 The order is important, as only the first usable "num_workers -
 min_num_lns_workers" subsolvers will be scheduled. You can see in the log
 which one are selected for a given run. All the others will be LNS if there
 is an objective, or randomized SAT search for pure satisfiability problems.

repeated string subsolvers = 207;

Returns:: The count of subsolvers.

getSubsolvers

java.lang.String getSubsolvers(int index)

 In multi-thread, the solver can be mainly seen as a portfolio of solvers
 with different parameters. This field indicates the names of the parameters
 that are used in multithread.

 See cp_model_search.cc to see a list of the names and the default value (if
 left empty) that looks like:
 - default_lp           (linearization_level:1)
 - fixed                (only if fixed search specified or scheduling)
 - no_lp                (linearization_level:0)
 - max_lp               (linearization_level:2)
 - pseudo_costs         (only if objective, change search heuristic)
 - reduced_costs        (only if objective, change search heuristic)
 - quick_restart        (kind of probing)
 - quick_restart_no_lp  (kind of probing with linearization_level:0)
 - lb_tree_search       (to improve lower bound, MIP like tree search)
 - probing              (continuous probing and shaving)

 Also, note that some set of parameters will be ignored if they do not make
 sense. For instance if there is no objective, pseudo_cost or reduced_cost
 search will be ignored. Core based search will only work if the objective
 has many terms. If there is no fixed strategy fixed will be ignored. And so
 on.

 The order is important, as only the first usable "num_workers -
 min_num_lns_workers" subsolvers will be scheduled. You can see in the log
 which one are selected for a given run. All the others will be LNS if there
 is an objective, or randomized SAT search for pure satisfiability problems.

repeated string subsolvers = 207;

Parameters:: index - The index of the element to return.
Returns:: The subsolvers at the given index.

getSubsolversBytes

com.google.protobuf.ByteString getSubsolversBytes(int index)

 In multi-thread, the solver can be mainly seen as a portfolio of solvers
 with different parameters. This field indicates the names of the parameters
 that are used in multithread.

 See cp_model_search.cc to see a list of the names and the default value (if
 left empty) that looks like:
 - default_lp           (linearization_level:1)
 - fixed                (only if fixed search specified or scheduling)
 - no_lp                (linearization_level:0)
 - max_lp               (linearization_level:2)
 - pseudo_costs         (only if objective, change search heuristic)
 - reduced_costs        (only if objective, change search heuristic)
 - quick_restart        (kind of probing)
 - quick_restart_no_lp  (kind of probing with linearization_level:0)
 - lb_tree_search       (to improve lower bound, MIP like tree search)
 - probing              (continuous probing and shaving)

 Also, note that some set of parameters will be ignored if they do not make
 sense. For instance if there is no objective, pseudo_cost or reduced_cost
 search will be ignored. Core based search will only work if the objective
 has many terms. If there is no fixed strategy fixed will be ignored. And so
 on.

 The order is important, as only the first usable "num_workers -
 min_num_lns_workers" subsolvers will be scheduled. You can see in the log
 which one are selected for a given run. All the others will be LNS if there
 is an objective, or randomized SAT search for pure satisfiability problems.

repeated string subsolvers = 207;

Parameters:: index - The index of the value to return.
Returns:: The bytes of the subsolvers at the given index.

getExtraSubsolversList

java.util.List<java.lang.String> getExtraSubsolversList()

 A convenient way to add more workers types.
 These will be added at the beginning of the list.

repeated string extra_subsolvers = 219;

Returns:: A list containing the extraSubsolvers.

getExtraSubsolversCount

int getExtraSubsolversCount()

 A convenient way to add more workers types.
 These will be added at the beginning of the list.

repeated string extra_subsolvers = 219;

Returns:: The count of extraSubsolvers.

getExtraSubsolvers
```
java.lang.String getExtraSubsolvers(int index)
```
```
 A convenient way to add more workers types.
 These will be added at the beginning of the list.
 
```
repeated string extra_subsolvers = 219;
Parameters:

index - The index of the element to return.

Returns:

The extraSubsolvers at the given index.

getExtraSubsolversBytes
```
com.google.protobuf.ByteString getExtraSubsolversBytes(int index)
```
```
 A convenient way to add more workers types.
 These will be added at the beginning of the list.
 
```
repeated string extra_subsolvers = 219;
Parameters:

index - The index of the value to return.

Returns:

The bytes of the extraSubsolvers at the given index.

getIgnoreSubsolversList

java.util.List<java.lang.String> getIgnoreSubsolversList()

 Rather than fully specifying subsolvers, it is often convenient to just
 remove the ones that are not useful on a given problem.

repeated string ignore_subsolvers = 209;

Returns:: A list containing the ignoreSubsolvers.

getIgnoreSubsolversCount

int getIgnoreSubsolversCount()

 Rather than fully specifying subsolvers, it is often convenient to just
 remove the ones that are not useful on a given problem.

repeated string ignore_subsolvers = 209;

Returns:: The count of ignoreSubsolvers.

getIgnoreSubsolvers

java.lang.String getIgnoreSubsolvers(int index)

 Rather than fully specifying subsolvers, it is often convenient to just
 remove the ones that are not useful on a given problem.

repeated string ignore_subsolvers = 209;

Parameters:: index - The index of the element to return.
Returns:: The ignoreSubsolvers at the given index.

getIgnoreSubsolversBytes

com.google.protobuf.ByteString getIgnoreSubsolversBytes(int index)

 Rather than fully specifying subsolvers, it is often convenient to just
 remove the ones that are not useful on a given problem.

repeated string ignore_subsolvers = 209;

Parameters:: index - The index of the value to return.
Returns:: The bytes of the ignoreSubsolvers at the given index.