Inheritance diagram for com.google.ortools.glop.GlopParameters.Builder:

Public Member Functions
Builder	clear ()

com.google.protobuf.Descriptors.Descriptor	getDescriptorForType ()

com.google.ortools.glop.GlopParameters	getDefaultInstanceForType ()

com.google.ortools.glop.GlopParameters	build ()

com.google.ortools.glop.GlopParameters	buildPartial ()

Builder	mergeFrom (com.google.protobuf.Message other)

Builder	mergeFrom (com.google.ortools.glop.GlopParameters other)

final boolean	isInitialized ()

Builder	mergeFrom (com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException

boolean	hasScalingMethod ()

com.google.ortools.glop.GlopParameters.ScalingAlgorithm	getScalingMethod ()

Builder	setScalingMethod (com.google.ortools.glop.GlopParameters.ScalingAlgorithm value)

Builder	clearScalingMethod ()

boolean	hasFeasibilityRule ()

com.google.ortools.glop.GlopParameters.PricingRule	getFeasibilityRule ()

Builder	setFeasibilityRule (com.google.ortools.glop.GlopParameters.PricingRule value)

Builder	clearFeasibilityRule ()

boolean	hasOptimizationRule ()

com.google.ortools.glop.GlopParameters.PricingRule	getOptimizationRule ()

Builder	setOptimizationRule (com.google.ortools.glop.GlopParameters.PricingRule value)

Builder	clearOptimizationRule ()

boolean	hasRefactorizationThreshold ()

double	getRefactorizationThreshold ()

Builder	setRefactorizationThreshold (double value)

Builder	clearRefactorizationThreshold ()

boolean	hasRecomputeReducedCostsThreshold ()

double	getRecomputeReducedCostsThreshold ()

Builder	setRecomputeReducedCostsThreshold (double value)

Builder	clearRecomputeReducedCostsThreshold ()

boolean	hasRecomputeEdgesNormThreshold ()

double	getRecomputeEdgesNormThreshold ()

Builder	setRecomputeEdgesNormThreshold (double value)

Builder	clearRecomputeEdgesNormThreshold ()

boolean	hasPrimalFeasibilityTolerance ()

double	getPrimalFeasibilityTolerance ()

Builder	setPrimalFeasibilityTolerance (double value)

Builder	clearPrimalFeasibilityTolerance ()

boolean	hasDualFeasibilityTolerance ()

double	getDualFeasibilityTolerance ()

Builder	setDualFeasibilityTolerance (double value)

Builder	clearDualFeasibilityTolerance ()

boolean	hasRatioTestZeroThreshold ()

double	getRatioTestZeroThreshold ()

Builder	setRatioTestZeroThreshold (double value)

Builder	clearRatioTestZeroThreshold ()

boolean	hasHarrisToleranceRatio ()

double	getHarrisToleranceRatio ()

Builder	setHarrisToleranceRatio (double value)

Builder	clearHarrisToleranceRatio ()

boolean	hasSmallPivotThreshold ()

double	getSmallPivotThreshold ()

Builder	setSmallPivotThreshold (double value)

Builder	clearSmallPivotThreshold ()

boolean	hasMinimumAcceptablePivot ()

double	getMinimumAcceptablePivot ()

Builder	setMinimumAcceptablePivot (double value)

Builder	clearMinimumAcceptablePivot ()

boolean	hasDropTolerance ()

double	getDropTolerance ()

Builder	setDropTolerance (double value)

Builder	clearDropTolerance ()

boolean	hasUseScaling ()

boolean	getUseScaling ()

Builder	setUseScaling (boolean value)

Builder	clearUseScaling ()

boolean	hasCostScaling ()

com.google.ortools.glop.GlopParameters.CostScalingAlgorithm	getCostScaling ()

Builder	setCostScaling (com.google.ortools.glop.GlopParameters.CostScalingAlgorithm value)

Builder	clearCostScaling ()

boolean	hasInitialBasis ()

com.google.ortools.glop.GlopParameters.InitialBasisHeuristic	getInitialBasis ()

Builder	setInitialBasis (com.google.ortools.glop.GlopParameters.InitialBasisHeuristic value)

Builder	clearInitialBasis ()

boolean	hasUseTransposedMatrix ()

boolean	getUseTransposedMatrix ()

Builder	setUseTransposedMatrix (boolean value)

Builder	clearUseTransposedMatrix ()

boolean	hasBasisRefactorizationPeriod ()

int	getBasisRefactorizationPeriod ()

Builder	setBasisRefactorizationPeriod (int value)

Builder	clearBasisRefactorizationPeriod ()

boolean	hasDynamicallyAdjustRefactorizationPeriod ()

boolean	getDynamicallyAdjustRefactorizationPeriod ()

Builder	setDynamicallyAdjustRefactorizationPeriod (boolean value)

Builder	clearDynamicallyAdjustRefactorizationPeriod ()

boolean	hasSolveDualProblem ()

com.google.ortools.glop.GlopParameters.SolverBehavior	getSolveDualProblem ()

Builder	setSolveDualProblem (com.google.ortools.glop.GlopParameters.SolverBehavior value)

Builder	clearSolveDualProblem ()

boolean	hasDualizerThreshold ()

double	getDualizerThreshold ()

Builder	setDualizerThreshold (double value)

Builder	clearDualizerThreshold ()

boolean	hasSolutionFeasibilityTolerance ()

double	getSolutionFeasibilityTolerance ()

Builder	setSolutionFeasibilityTolerance (double value)

Builder	clearSolutionFeasibilityTolerance ()

boolean	hasProvideStrongOptimalGuarantee ()

boolean	getProvideStrongOptimalGuarantee ()

Builder	setProvideStrongOptimalGuarantee (boolean value)

Builder	clearProvideStrongOptimalGuarantee ()

boolean	hasChangeStatusToImprecise ()

boolean	getChangeStatusToImprecise ()

Builder	setChangeStatusToImprecise (boolean value)

Builder	clearChangeStatusToImprecise ()

boolean	hasMaxNumberOfReoptimizations ()

double	getMaxNumberOfReoptimizations ()

Builder	setMaxNumberOfReoptimizations (double value)

Builder	clearMaxNumberOfReoptimizations ()

boolean	hasLuFactorizationPivotThreshold ()

double	getLuFactorizationPivotThreshold ()

Builder	setLuFactorizationPivotThreshold (double value)

Builder	clearLuFactorizationPivotThreshold ()

boolean	hasMaxTimeInSeconds ()

double	getMaxTimeInSeconds ()

Builder	setMaxTimeInSeconds (double value)

Builder	clearMaxTimeInSeconds ()

boolean	hasMaxDeterministicTime ()

double	getMaxDeterministicTime ()

Builder	setMaxDeterministicTime (double value)

Builder	clearMaxDeterministicTime ()

boolean	hasMaxNumberOfIterations ()

long	getMaxNumberOfIterations ()

Builder	setMaxNumberOfIterations (long value)

Builder	clearMaxNumberOfIterations ()

boolean	hasMarkowitzZlatevParameter ()

int	getMarkowitzZlatevParameter ()

Builder	setMarkowitzZlatevParameter (int value)

Builder	clearMarkowitzZlatevParameter ()

boolean	hasMarkowitzSingularityThreshold ()

double	getMarkowitzSingularityThreshold ()

Builder	setMarkowitzSingularityThreshold (double value)

Builder	clearMarkowitzSingularityThreshold ()

boolean	hasUseDualSimplex ()

boolean	getUseDualSimplex ()

Builder	setUseDualSimplex (boolean value)

Builder	clearUseDualSimplex ()

boolean	hasAllowSimplexAlgorithmChange ()

boolean	getAllowSimplexAlgorithmChange ()

Builder	setAllowSimplexAlgorithmChange (boolean value)

Builder	clearAllowSimplexAlgorithmChange ()

boolean	hasDevexWeightsResetPeriod ()

int	getDevexWeightsResetPeriod ()

Builder	setDevexWeightsResetPeriod (int value)

Builder	clearDevexWeightsResetPeriod ()

boolean	hasUsePreprocessing ()

boolean	getUsePreprocessing ()

Builder	setUsePreprocessing (boolean value)

Builder	clearUsePreprocessing ()

boolean	hasUseMiddleProductFormUpdate ()

boolean	getUseMiddleProductFormUpdate ()

Builder	setUseMiddleProductFormUpdate (boolean value)

Builder	clearUseMiddleProductFormUpdate ()

boolean	hasInitializeDevexWithColumnNorms ()

boolean	getInitializeDevexWithColumnNorms ()

Builder	setInitializeDevexWithColumnNorms (boolean value)

Builder	clearInitializeDevexWithColumnNorms ()

boolean	hasExploitSingletonColumnInInitialBasis ()

boolean	getExploitSingletonColumnInInitialBasis ()

Builder	setExploitSingletonColumnInInitialBasis (boolean value)

Builder	clearExploitSingletonColumnInInitialBasis ()

boolean	hasDualSmallPivotThreshold ()

double	getDualSmallPivotThreshold ()

Builder	setDualSmallPivotThreshold (double value)

Builder	clearDualSmallPivotThreshold ()

boolean	hasPreprocessorZeroTolerance ()

double	getPreprocessorZeroTolerance ()

Builder	setPreprocessorZeroTolerance (double value)

Builder	clearPreprocessorZeroTolerance ()

boolean	hasObjectiveLowerLimit ()

double	getObjectiveLowerLimit ()

Builder	setObjectiveLowerLimit (double value)

Builder	clearObjectiveLowerLimit ()

boolean	hasObjectiveUpperLimit ()

double	getObjectiveUpperLimit ()

Builder	setObjectiveUpperLimit (double value)

Builder	clearObjectiveUpperLimit ()

boolean	hasDegenerateMinistepFactor ()

double	getDegenerateMinistepFactor ()

Builder	setDegenerateMinistepFactor (double value)

Builder	clearDegenerateMinistepFactor ()

boolean	hasRandomSeed ()

int	getRandomSeed ()

Builder	setRandomSeed (int value)

Builder	clearRandomSeed ()

boolean	hasNumOmpThreads ()

int	getNumOmpThreads ()

Builder	setNumOmpThreads (int value)

Builder	clearNumOmpThreads ()

boolean	hasPerturbCostsInDualSimplex ()

boolean	getPerturbCostsInDualSimplex ()

Builder	setPerturbCostsInDualSimplex (boolean value)

Builder	clearPerturbCostsInDualSimplex ()

boolean	hasUseDedicatedDualFeasibilityAlgorithm ()

boolean	getUseDedicatedDualFeasibilityAlgorithm ()

Builder	setUseDedicatedDualFeasibilityAlgorithm (boolean value)

Builder	clearUseDedicatedDualFeasibilityAlgorithm ()

boolean	hasRelativeCostPerturbation ()

double	getRelativeCostPerturbation ()

Builder	setRelativeCostPerturbation (double value)

Builder	clearRelativeCostPerturbation ()

boolean	hasRelativeMaxCostPerturbation ()

double	getRelativeMaxCostPerturbation ()

Builder	setRelativeMaxCostPerturbation (double value)

Builder	clearRelativeMaxCostPerturbation ()

boolean	hasInitialConditionNumberThreshold ()

double	getInitialConditionNumberThreshold ()

Builder	setInitialConditionNumberThreshold (double value)

Builder	clearInitialConditionNumberThreshold ()

boolean	hasLogSearchProgress ()

boolean	getLogSearchProgress ()

Builder	setLogSearchProgress (boolean value)

Builder	clearLogSearchProgress ()

boolean	hasLogToStdout ()

boolean	getLogToStdout ()

Builder	setLogToStdout (boolean value)

Builder	clearLogToStdout ()

boolean	hasCrossoverBoundSnappingDistance ()

double	getCrossoverBoundSnappingDistance ()

Builder	setCrossoverBoundSnappingDistance (double value)

Builder	clearCrossoverBoundSnappingDistance ()

boolean	hasPushToVertex ()

boolean	getPushToVertex ()

Builder	setPushToVertex (boolean value)

Builder	clearPushToVertex ()

boolean	hasUseImpliedFreePreprocessor ()

boolean	getUseImpliedFreePreprocessor ()

Builder	setUseImpliedFreePreprocessor (boolean value)

Builder	clearUseImpliedFreePreprocessor ()

boolean	hasMaxValidMagnitude ()

double	getMaxValidMagnitude ()

Builder	setMaxValidMagnitude (double value)

Builder	clearMaxValidMagnitude ()

boolean	hasDropMagnitude ()

double	getDropMagnitude ()

Builder	setDropMagnitude (double value)

Builder	clearDropMagnitude ()

boolean	hasDualPricePrioritizeNorm ()

boolean	getDualPricePrioritizeNorm ()

Builder	setDualPricePrioritizeNorm (boolean value)

Builder	clearDualPricePrioritizeNorm ()

Static Public Member Functions
static final com.google.protobuf.Descriptors.Descriptor	getDescriptor ()

Protected Member Functions
com.google.protobuf.GeneratedMessage.FieldAccessorTable	internalGetFieldAccessorTable ()

Detailed Description

next id = 72

Protobuf type operations_research.glop.GlopParameters

Definition at line 3942 of file GlopParameters.java.

Member Function Documentation

◆ build()

com.google.ortools.glop.GlopParameters com.google.ortools.glop.GlopParameters.Builder.build ( )

Definition at line 4047 of file GlopParameters.java.

◆ buildPartial()

com.google.ortools.glop.GlopParameters com.google.ortools.glop.GlopParameters.Builder.buildPartial ( )

Definition at line 4056 of file GlopParameters.java.

◆ clear()

Builder com.google.ortools.glop.GlopParameters.Builder.clear ( )

Definition at line 3970 of file GlopParameters.java.

◆ clearAllowSimplexAlgorithmChange()

Builder com.google.ortools.glop.GlopParameters.Builder.clearAllowSimplexAlgorithmChange ( )

During incremental solve, let the solver decide if it use the primal or
dual simplex algorithm depending on the current solution and on the new
problem. Note that even if this is true, the value of use_dual_simplex
still indicates the default algorithm that the solver will use.

optional bool allow_simplex_algorithm_change = 32 [default = false];

Returns: This builder for chaining.

Definition at line 7115 of file GlopParameters.java.

◆ clearBasisRefactorizationPeriod()

Builder com.google.ortools.glop.GlopParameters.Builder.clearBasisRefactorizationPeriod ( )

Number of iterations between two basis refactorizations. Note that various
conditions in the algorithm may trigger a refactorization before this
period is reached. Set this to 0 if you want to refactorize at each step.

optional int32 basis_refactorization_period = 19 [default = 64];

Returns: This builder for chaining.

Definition at line 6069 of file GlopParameters.java.

◆ clearChangeStatusToImprecise()

Builder com.google.ortools.glop.GlopParameters.Builder.clearChangeStatusToImprecise ( )

If true, the internal API will change the return status to imprecise if the
solution does not respect the internal tolerances.

optional bool change_status_to_imprecise = 58 [default = true];

Returns: This builder for chaining.

Definition at line 6539 of file GlopParameters.java.

◆ clearCostScaling()

Builder com.google.ortools.glop.GlopParameters.Builder.clearCostScaling ( )

optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Returns: This builder for chaining.

Definition at line 5879 of file GlopParameters.java.

◆ clearCrossoverBoundSnappingDistance()

Builder com.google.ortools.glop.GlopParameters.Builder.clearCrossoverBoundSnappingDistance ( )

If the starting basis contains FREE variable with bounds, we will move
any such variable to their closer bounds if the distance is smaller than
this parameter.

The starting statuses can contains FREE variables with bounds, if a user
set it like this externally. Also, any variable with an initial BASIC
status that was not kept in the initial basis is marked as FREE before this
step is applied.

Note that by default a FREE variable is assumed to be zero unless a
starting value was specified via SetStartingVariableValuesForNextSolve().

Note that, at the end of the solve, some of these FREE variable with bounds
and an interior point value might still be left in the final solution.
Enable push_to_vertex to clean these up.

optional double crossover_bound_snapping_distance = 64 [default = inf];

Returns: This builder for chaining.

Definition at line 8547 of file GlopParameters.java.

◆ clearDegenerateMinistepFactor()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDegenerateMinistepFactor ( )

During a degenerate iteration, the more conservative approach is to do a
step of length zero (while shifting the bound of the leaving variable).
That is, the variable values are unchanged for the primal simplex or the
reduced cost are unchanged for the dual simplex. However, instead of doing
a step of length zero, it seems to be better on degenerate problems to do a
small positive step. This is what is recommended in the EXPAND procedure
described in:
P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright. "A practical anti-
cycling procedure for linearly constrained optimization".
Mathematical Programming, 45:437&#92;u2013474, 1989.

Here, during a degenerate iteration we do a small positive step of this
factor times the primal (resp. dual) tolerance. In the primal simplex, this
may effectively push variable values (very slightly) further out of their
bounds (resp. reduced costs for the dual simplex).

Setting this to zero reverts to the more conservative approach of a zero
step during degenerate iterations.

optional double degenerate_ministep_factor = 42 [default = 0.01];

Returns: This builder for chaining.

Definition at line 7819 of file GlopParameters.java.

◆ clearDevexWeightsResetPeriod()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDevexWeightsResetPeriod ( )

Devex weights will be reset to 1.0 after that number of updates.

optional int32 devex_weights_reset_period = 33 [default = 150];

Returns: This builder for chaining.

Definition at line 7171 of file GlopParameters.java.

◆ clearDropMagnitude()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDropMagnitude ( )

Value in the input LP lower than this will be ignored. This is similar to
drop_tolerance but more aggressive as this is used before scaling. This is
mainly here to avoid underflow and have simpler invariant in the code, like
a * b == 0 iff a or b is zero and things like this.

optional double drop_magnitude = 71 [default = 1e-30];

Returns: This builder for chaining.

Definition at line 8823 of file GlopParameters.java.

◆ clearDropTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDropTolerance ( )

In order to increase the sparsity of the manipulated vectors, floating
point values with a magnitude smaller than this parameter are set to zero
(only in some places). This parameter should be positive or zero.

optional double drop_tolerance = 52 [default = 1e-14];

Returns: This builder for chaining.

Definition at line 5777 of file GlopParameters.java.

◆ clearDualFeasibilityTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDualFeasibilityTolerance ( )

Variables whose reduced costs have an absolute value smaller than this
tolerance are not considered as entering candidates. That is they do not
take part in deciding whether a solution is dual-feasible or not.

Note that this value can temporarily increase during the execution of the
algorithm if the estimated precision of the reduced costs is higher than
this tolerance. Note also that we scale the costs (in the presolve step) so
that the cost magnitude range contains one.

This is also known as the optimality tolerance in other solvers.

optional double dual_feasibility_tolerance = 11 [default = 1e-08];

Returns: This builder for chaining.

Definition at line 5413 of file GlopParameters.java.

◆ clearDualizerThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDualizerThreshold ( )

When solve_dual_problem is LET_SOLVER_DECIDE, take the dual if the number
of constraints of the problem is more than this threshold times the number
of variables.

optional double dualizer_threshold = 21 [default = 1.5];

Returns: This builder for chaining.

Definition at line 6271 of file GlopParameters.java.

◆ clearDualPricePrioritizeNorm()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDualPricePrioritizeNorm ( )

On some problem like stp3d or pds-100 this makes a huge difference in
speed and number of iterations of the dual simplex.

optional bool dual_price_prioritize_norm = 69 [default = false];

Returns: This builder for chaining.

Definition at line 8883 of file GlopParameters.java.

◆ clearDualSmallPivotThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDualSmallPivotThreshold ( )

Like small_pivot_threshold but for the dual simplex. This is needed because
the dual algorithm does not interpret this value in the same way.
TODO(user): Clean this up and use the same small pivot detection.

optional double dual_small_pivot_threshold = 38 [default = 0.0001];

Returns: This builder for chaining.

Definition at line 7491 of file GlopParameters.java.

◆ clearDynamicallyAdjustRefactorizationPeriod()

Builder com.google.ortools.glop.GlopParameters.Builder.clearDynamicallyAdjustRefactorizationPeriod ( )

If this is true, then basis_refactorization_period becomes a lower bound on
the number of iterations between two refactorization (provided there is no
numerical accuracy issues). Depending on the estimated time to refactorize
vs the extra time spend in each solves because of the LU update, we try to
balance the two times.

optional bool dynamically_adjust_refactorization_period = 63 [default = true];

Returns: This builder for chaining.

Definition at line 6141 of file GlopParameters.java.

◆ clearExploitSingletonColumnInInitialBasis()

Builder com.google.ortools.glop.GlopParameters.Builder.clearExploitSingletonColumnInInitialBasis ( )

Whether or not we exploit the singleton columns already present in the
problem when we create the initial basis.

optional bool exploit_singleton_column_in_initial_basis = 37 [default = true];

Returns: This builder for chaining.

Definition at line 7427 of file GlopParameters.java.

◆ clearFeasibilityRule()

Builder com.google.ortools.glop.GlopParameters.Builder.clearFeasibilityRule ( )

PricingRule to use during the feasibility phase.

optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];

Returns: This builder for chaining.

Definition at line 4963 of file GlopParameters.java.

◆ clearHarrisToleranceRatio()

Builder com.google.ortools.glop.GlopParameters.Builder.clearHarrisToleranceRatio ( )

This impacts the ratio test and indicates by how much we allow a basic
variable value that we move to go out of bounds. The value should be in
[0.0, 1.0) and should be interpreted as a ratio of the
primal_feasibility_tolerance. Setting this to 0.0 basically disables the
Harris ratio test while setting this too close to 1.0 will make it
difficult to keep the variable values inside their bounds modulo the
primal_feasibility_tolerance.

Note that the same comment applies to the dual simplex ratio test. There,
we allow the reduced costs to be of an infeasible sign by as much as this
ratio times the dual_feasibility_tolerance.

optional double harris_tolerance_ratio = 13 [default = 0.5];

Returns: This builder for chaining.

Definition at line 5589 of file GlopParameters.java.

◆ clearInitialBasis()

Builder com.google.ortools.glop.GlopParameters.Builder.clearInitialBasis ( )

What heuristic is used to try to replace the fixed slack columns in the
initial basis of the primal simplex.

optional .operations_research.glop.GlopParameters.InitialBasisHeuristic initial_basis = 17 [default = TRIANGULAR];

Returns: This builder for chaining.

Definition at line 5941 of file GlopParameters.java.

◆ clearInitialConditionNumberThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearInitialConditionNumberThreshold ( )

If our upper bound on the condition number of the initial basis (from our
heurisitic or a warm start) is above this threshold, we revert to an all
slack basis.

optional double initial_condition_number_threshold = 59 [default = 1e+50];

Returns: This builder for chaining.

Definition at line 8315 of file GlopParameters.java.

◆ clearInitializeDevexWithColumnNorms()

Builder com.google.ortools.glop.GlopParameters.Builder.clearInitializeDevexWithColumnNorms ( )

Whether we initialize devex weights to 1.0 or to the norms of the matrix
columns.

optional bool initialize_devex_with_column_norms = 36 [default = true];

Returns: This builder for chaining.

Definition at line 7367 of file GlopParameters.java.

◆ clearLogSearchProgress()

Builder com.google.ortools.glop.GlopParameters.Builder.clearLogSearchProgress ( )

If true, logs the progress of a solve to LOG(INFO). Note that the same
messages can also be turned on by displaying logs at level 1 for the
relevant files.

optional bool log_search_progress = 61 [default = false];

Returns: This builder for chaining.

Definition at line 8379 of file GlopParameters.java.

◆ clearLogToStdout()

Builder com.google.ortools.glop.GlopParameters.Builder.clearLogToStdout ( )

If true, logs will be displayed to stdout instead of using Google log info.

optional bool log_to_stdout = 66 [default = true];

Returns: This builder for chaining.

Definition at line 8435 of file GlopParameters.java.

◆ clearLuFactorizationPivotThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearLuFactorizationPivotThreshold ( )

Threshold for LU-factorization: for stability reasons, the magnitude of the
chosen pivot at a given step is guaranteed to be greater than this
threshold times the maximum magnitude of all the possible pivot choices in
the same column. The value must be in [0,1].

optional double lu_factorization_pivot_threshold = 25 [default = 0.01];

Returns: This builder for chaining.

Definition at line 6675 of file GlopParameters.java.

◆ clearMarkowitzSingularityThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMarkowitzSingularityThreshold ( )

If a pivot magnitude is smaller than this during the Markowitz LU
factorization, then the matrix is assumed to be singular. Note that
this is an absolute threshold and is not relative to the other possible
pivots on the same column (see lu_factorization_pivot_threshold).

optional double markowitz_singularity_threshold = 30 [default = 1e-15];

Returns: This builder for chaining.

Definition at line 6991 of file GlopParameters.java.

◆ clearMarkowitzZlatevParameter()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMarkowitzZlatevParameter ( )

How many columns do we look at in the Markowitz pivoting rule to find
a good pivot. See markowitz.h.

optional int32 markowitz_zlatev_parameter = 29 [default = 3];

Returns: This builder for chaining.

Definition at line 6923 of file GlopParameters.java.

◆ clearMaxDeterministicTime()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMaxDeterministicTime ( )

Maximum deterministic time allowed to solve a problem. The deterministic
time is more or less correlated to the running time, and its unit should
be around the second (at least on a Xeon(R) CPU E5-1650 v2 &#64; 3.50GHz).

TODO(user): Improve the correlation.

optional double max_deterministic_time = 45 [default = inf];

Returns: This builder for chaining.

Definition at line 6803 of file GlopParameters.java.

◆ clearMaxNumberOfIterations()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMaxNumberOfIterations ( )

Maximum number of simplex iterations to solve a problem.
A value of -1 means no limit.

optional int64 max_number_of_iterations = 27 [default = -1];

Returns: This builder for chaining.

Definition at line 6863 of file GlopParameters.java.

◆ clearMaxNumberOfReoptimizations()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMaxNumberOfReoptimizations ( )

When the solution of phase II is imprecise, we re-run the phase II with the
opposite algorithm from that imprecise solution (i.e., if primal or dual
simplex was used, we use dual or primal simplex, respectively). We repeat
such re-optimization until the solution is precise, or we hit this limit.

optional double max_number_of_reoptimizations = 56 [default = 40];

Returns: This builder for chaining.

Definition at line 6607 of file GlopParameters.java.

◆ clearMaxTimeInSeconds()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMaxTimeInSeconds ( )

Maximum time allowed in seconds to solve a problem.

optional double max_time_in_seconds = 26 [default = inf];

Returns: This builder for chaining.

Definition at line 6731 of file GlopParameters.java.

◆ clearMaxValidMagnitude()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMaxValidMagnitude ( )

Any finite values in the input LP must be below this threshold, otherwise
the model will be reported invalid. This is needed to avoid floating point
overflow when evaluating bounds * coeff for instance. In practice, users
shouldn't use super large values in an LP. With the default threshold, even
evaluating large constraint with variables at their bound shouldn't cause
any overflow.

optional double max_valid_magnitude = 70 [default = 1e+30];

Returns: This builder for chaining.

Definition at line 8755 of file GlopParameters.java.

◆ clearMinimumAcceptablePivot()

Builder com.google.ortools.glop.GlopParameters.Builder.clearMinimumAcceptablePivot ( )

We never follow a basis change with a pivot under this threshold.

optional double minimum_acceptable_pivot = 15 [default = 1e-06];

Returns: This builder for chaining.

Definition at line 5713 of file GlopParameters.java.

◆ clearNumOmpThreads()

Builder com.google.ortools.glop.GlopParameters.Builder.clearNumOmpThreads ( )

Number of threads in the OMP parallel sections. If left to 1, the code will
not create any OMP threads and will remain single-threaded.

optional int32 num_omp_threads = 44 [default = 1];

Returns: This builder for chaining.

Definition at line 7991 of file GlopParameters.java.

◆ clearObjectiveLowerLimit()

Builder com.google.ortools.glop.GlopParameters.Builder.clearObjectiveLowerLimit ( )

The solver will stop as soon as it has proven that the objective is smaller
than objective_lower_limit or greater than objective_upper_limit. Depending
on the simplex algorithm (primal or dual) and the optimization direction,
note that only one bound will be used at the time.

Important: The solver does not add any tolerances to these values, and as
soon as the objective (as computed by the solver, so with some imprecision)
crosses one of these bounds (strictly), the search will stop. It is up to
the client to add any tolerance if needed.

optional double objective_lower_limit = 40 [default = -inf];

Returns: This builder for chaining.

Definition at line 7655 of file GlopParameters.java.

◆ clearObjectiveUpperLimit()

Builder com.google.ortools.glop.GlopParameters.Builder.clearObjectiveUpperLimit ( )

optional double objective_upper_limit = 41 [default = inf];

Returns: This builder for chaining.

Definition at line 7695 of file GlopParameters.java.

◆ clearOptimizationRule()

Builder com.google.ortools.glop.GlopParameters.Builder.clearOptimizationRule ( )

PricingRule to use during the optimization phase.

optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];

Returns: This builder for chaining.

Definition at line 5021 of file GlopParameters.java.

◆ clearPerturbCostsInDualSimplex()

Builder com.google.ortools.glop.GlopParameters.Builder.clearPerturbCostsInDualSimplex ( )

When this is true, then the costs are randomly perturbed before the dual
simplex is even started. This has been shown to improve the dual simplex
performance. For a good reference, see Huangfu Q (2013) "High performance
simplex solver", Ph.D, dissertation, University of Edinburgh.

optional bool perturb_costs_in_dual_simplex = 53 [default = false];

Returns: This builder for chaining.

Definition at line 8059 of file GlopParameters.java.

◆ clearPreprocessorZeroTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.clearPreprocessorZeroTolerance ( )

A floating point tolerance used by the preprocessors. This is used for
things like detecting if two columns/rows are proportional or if an
interval is empty.

Note that the preprocessors also use solution_feasibility_tolerance() to
detect if a problem is infeasible.

optional double preprocessor_zero_tolerance = 39 [default = 1e-09];

Returns: This builder for chaining.

Definition at line 7567 of file GlopParameters.java.

◆ clearPrimalFeasibilityTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.clearPrimalFeasibilityTolerance ( )

This tolerance indicates by how much we allow the variable values to go out
of bounds and still consider the current solution primal-feasible. We also
use the same tolerance for the error A.x - b. Note that the two errors are
closely related if A is scaled in such a way that the greatest coefficient
magnitude on each column is 1.0.

This is also simply called feasibility tolerance in other solvers.

optional double primal_feasibility_tolerance = 10 [default = 1e-08];

Returns: This builder for chaining.

Definition at line 5321 of file GlopParameters.java.

◆ clearProvideStrongOptimalGuarantee()

Builder com.google.ortools.glop.GlopParameters.Builder.clearProvideStrongOptimalGuarantee ( )

If true, then when the solver returns a solution with an OPTIMAL status,
we can guarantee that:
- The primal variable are in their bounds.
- The dual variable are in their bounds.
- If we modify each component of the right-hand side a bit and each
component of the objective function a bit, then the pair (primal values,
dual values) is an EXACT optimal solution of the perturbed problem.
- The modifications above are smaller than the associated tolerances as
defined in the comment for solution_feasibility_tolerance (*).

(*): This is the only place where the guarantee is not tight since we
compute the upper bounds with scalar product of the primal/dual
solution and the initial problem coefficients with only double precision.

Note that whether or not this option is true, we still check the
primal/dual infeasibility and objective gap. However if it is false, we
don't move the primal/dual values within their bounds and leave them
untouched.

optional bool provide_strong_optimal_guarantee = 24 [default = true];

Returns: This builder for chaining.

Definition at line 6479 of file GlopParameters.java.

◆ clearPushToVertex()

Builder com.google.ortools.glop.GlopParameters.Builder.clearPushToVertex ( )

If the optimization phases finishes with super-basic variables (i.e.,
variables that either 1) have bounds but are FREE in the basis, or 2) have
no bounds and are FREE in the basis at a nonzero value), then run a "push"
phase to push these variables to bounds, obtaining a vertex solution. Note
this situation can happen only if a starting value was specified via
SetStartingVariableValuesForNextSolve().

optional bool push_to_vertex = 65 [default = true];

Returns: This builder for chaining.

Definition at line 8623 of file GlopParameters.java.

◆ clearRandomSeed()

Builder com.google.ortools.glop.GlopParameters.Builder.clearRandomSeed ( )

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.
Note that this may lead to a different solution, for example a different
optimal basis.

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.

Also note that the solver is fully deterministic: two runs of the same
binary, on the same machine, on the exact same data and with the same
parameters will go through the exact same iterations. If they hit a time
limit, they might of course yield different results because one will have
advanced farther than the other.

optional int32 random_seed = 43 [default = 1];

Returns: This builder for chaining.

Definition at line 7931 of file GlopParameters.java.

◆ clearRatioTestZeroThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearRatioTestZeroThreshold ( )

During the primal simplex (resp. dual simplex), the coefficients of the
direction (resp. update row) with a magnitude lower than this threshold are
not considered during the ratio test. This tolerance is related to the
precision at which a Solve() involving the basis matrix can be performed.

TODO(user): Automatically increase it when we detect that the precision
of the Solve() is worse than this.

optional double ratio_test_zero_threshold = 12 [default = 1e-09];

Returns: This builder for chaining.

Definition at line 5493 of file GlopParameters.java.

◆ clearRecomputeEdgesNormThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearRecomputeEdgesNormThreshold ( )

Note that the threshold is a relative error on the actual norm (not the
squared one) and that edge norms are always greater than 1. Recomputing
norms is a really expensive operation and a large threshold is ok since
this doesn't impact directly the solution but just the entering variable
choice.

optional double recompute_edges_norm_threshold = 9 [default = 100];

Returns: This builder for chaining.

Definition at line 5241 of file GlopParameters.java.

◆ clearRecomputeReducedCostsThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearRecomputeReducedCostsThreshold ( )

We estimate the accuracy of the iteratively computed reduced costs. If
it falls below this threshold, we reinitialize them from scratch. Note
that such an operation is pretty fast, so we can use a low threshold.
It is important to have a good accuracy here (better than the
dual_feasibility_tolerance below) to be sure of the sign of such a cost.

optional double recompute_reduced_costs_threshold = 8 [default = 1e-08];

Returns: This builder for chaining.

Definition at line 5169 of file GlopParameters.java.

◆ clearRefactorizationThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearRefactorizationThreshold ( )

We estimate the factorization accuracy of B during each pivot by using
the fact that we can compute the pivot coefficient in two ways:
- From direction[leaving_row].
- From update_row[entering_column].
If the two values have a relative difference above this threshold, we
trigger a refactorization.

optional double refactorization_threshold = 6 [default = 1e-09];

Returns: This builder for chaining.

Definition at line 5097 of file GlopParameters.java.

◆ clearRelativeCostPerturbation()

Builder com.google.ortools.glop.GlopParameters.Builder.clearRelativeCostPerturbation ( )

The magnitude of the cost perturbation is given by
RandomIn(1.0, 2.0) * (
relative_cost_perturbation * cost
+ relative_max_cost_perturbation * max_cost);

optional double relative_cost_perturbation = 54 [default = 1e-05];

Returns: This builder for chaining.

Definition at line 8211 of file GlopParameters.java.

◆ clearRelativeMaxCostPerturbation()

Builder com.google.ortools.glop.GlopParameters.Builder.clearRelativeMaxCostPerturbation ( )

optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Returns: This builder for chaining.

Definition at line 8251 of file GlopParameters.java.

◆ clearScalingMethod()

Builder com.google.ortools.glop.GlopParameters.Builder.clearScalingMethod ( )

optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Returns: This builder for chaining.

Definition at line 4905 of file GlopParameters.java.

◆ clearSmallPivotThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.clearSmallPivotThreshold ( )

When we choose the leaving variable, we want to avoid small pivot because
they are the less precise and may cause numerical instabilities. For a
pivot under this threshold times the infinity norm of the direction, we try
various countermeasures in order to avoid using it.

optional double small_pivot_threshold = 14 [default = 1e-06];

Returns: This builder for chaining.

Definition at line 5657 of file GlopParameters.java.

◆ clearSolutionFeasibilityTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.clearSolutionFeasibilityTolerance ( )

When the problem status is OPTIMAL, we check the optimality using this
relative tolerance and change the status to IMPRECISE if an issue is
detected.

The tolerance is "relative" in the sense that our thresholds are:
- tolerance * max(1.0, abs(bound)) for crossing a given bound.
- tolerance * max(1.0, abs(cost)) for an infeasible reduced cost.
- tolerance for an infeasible dual value.

optional double solution_feasibility_tolerance = 22 [default = 1e-06];

Returns: This builder for chaining.

Definition at line 6355 of file GlopParameters.java.

◆ clearSolveDualProblem()

Builder com.google.ortools.glop.GlopParameters.Builder.clearSolveDualProblem ( )

Whether or not we solve the dual of the given problem.
With a value of auto, the algorithm decide which approach is probably the
fastest depending on the problem dimensions (see dualizer_threshold).

optional .operations_research.glop.GlopParameters.SolverBehavior solve_dual_problem = 20 [default = LET_SOLVER_DECIDE];

Returns: This builder for chaining.

Definition at line 6207 of file GlopParameters.java.

◆ clearUseDedicatedDualFeasibilityAlgorithm()

Builder com.google.ortools.glop.GlopParameters.Builder.clearUseDedicatedDualFeasibilityAlgorithm ( )

We have two possible dual phase I algorithms. Both work on an LP that
minimize the sum of dual infeasiblities. One use dedicated code (when this
param is true), the other one use exactly the same code as the dual phase
II but on an auxiliary problem where the variable bounds of the original
problem are changed.

TODO(user): For now we have both, but ideally the non-dedicated version
will win since it is a lot less code to maintain.

optional bool use_dedicated_dual_feasibility_algorithm = 62 [default = true];

Returns: This builder for chaining.

Definition at line 8143 of file GlopParameters.java.

◆ clearUseDualSimplex()

Builder com.google.ortools.glop.GlopParameters.Builder.clearUseDualSimplex ( )

Whether or not we use the dual simplex algorithm instead of the primal.

optional bool use_dual_simplex = 31 [default = false];

Returns: This builder for chaining.

Definition at line 7047 of file GlopParameters.java.

◆ clearUseImpliedFreePreprocessor()

Builder com.google.ortools.glop.GlopParameters.Builder.clearUseImpliedFreePreprocessor ( )

If presolve runs, include the pass that detects implied free variables.

optional bool use_implied_free_preprocessor = 67 [default = true];

Returns: This builder for chaining.

Definition at line 8679 of file GlopParameters.java.

◆ clearUseMiddleProductFormUpdate()

Builder com.google.ortools.glop.GlopParameters.Builder.clearUseMiddleProductFormUpdate ( )

Whether or not to use the middle product form update rather than the
standard eta LU update. The middle form product update should be a lot more
efficient (close to the Forrest-Tomlin update, a bit slower but easier to
implement). See for more details:
Qi Huangfu, J. A. Julian Hall, "Novel update techniques for the revised
simplex method", 28 january 2013, Technical Report ERGO-13-0001
http://www.maths.ed.ac.uk/hall/HuHa12/ERGO-13-001.pdf

optional bool use_middle_product_form_update = 35 [default = true];

Returns: This builder for chaining.

Definition at line 7307 of file GlopParameters.java.

◆ clearUsePreprocessing()

Builder com.google.ortools.glop.GlopParameters.Builder.clearUsePreprocessing ( )

Whether or not we use advanced preprocessing techniques.

optional bool use_preprocessing = 34 [default = true];

Returns: This builder for chaining.

Definition at line 7227 of file GlopParameters.java.

◆ clearUseScaling()

Builder com.google.ortools.glop.GlopParameters.Builder.clearUseScaling ( )

Whether or not we scale the matrix A so that the maximum coefficient on
each line and each column is 1.0.

optional bool use_scaling = 16 [default = true];

Returns: This builder for chaining.

Definition at line 5837 of file GlopParameters.java.

◆ clearUseTransposedMatrix()

Builder com.google.ortools.glop.GlopParameters.Builder.clearUseTransposedMatrix ( )

Whether or not we keep a transposed version of the matrix A to speed-up the
pricing at the cost of extra memory and the initial tranposition
computation.

optional bool use_transposed_matrix = 18 [default = true];

Returns: This builder for chaining.

Definition at line 6005 of file GlopParameters.java.

◆ getAllowSimplexAlgorithmChange()

boolean com.google.ortools.glop.GlopParameters.Builder.getAllowSimplexAlgorithmChange ( )

During incremental solve, let the solver decide if it use the primal or
dual simplex algorithm depending on the current solution and on the new
problem. Note that even if this is true, the value of use_dual_simplex
still indicates the default algorithm that the solver will use.

optional bool allow_simplex_algorithm_change = 32 [default = false];

Returns: The allowSimplexAlgorithmChange.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7082 of file GlopParameters.java.

◆ getBasisRefactorizationPeriod()

int com.google.ortools.glop.GlopParameters.Builder.getBasisRefactorizationPeriod ( )

Number of iterations between two basis refactorizations. Note that various
conditions in the algorithm may trigger a refactorization before this
period is reached. Set this to 0 if you want to refactorize at each step.

optional int32 basis_refactorization_period = 19 [default = 64];

Returns: The basisRefactorizationPeriod.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6038 of file GlopParameters.java.

◆ getChangeStatusToImprecise()

boolean com.google.ortools.glop.GlopParameters.Builder.getChangeStatusToImprecise ( )

If true, the internal API will change the return status to imprecise if the
solution does not respect the internal tolerances.

optional bool change_status_to_imprecise = 58 [default = true];

Returns: The changeStatusToImprecise.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6510 of file GlopParameters.java.

◆ getCostScaling()

com.google.ortools.glop.GlopParameters.CostScalingAlgorithm com.google.ortools.glop.GlopParameters.Builder.getCostScaling ( )

optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Returns: The costScaling.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5857 of file GlopParameters.java.

◆ getCrossoverBoundSnappingDistance()

double com.google.ortools.glop.GlopParameters.Builder.getCrossoverBoundSnappingDistance ( )

If the starting basis contains FREE variable with bounds, we will move
any such variable to their closer bounds if the distance is smaller than
this parameter.

The starting statuses can contains FREE variables with bounds, if a user
set it like this externally. Also, any variable with an initial BASIC
status that was not kept in the initial basis is marked as FREE before this
step is applied.

Note that by default a FREE variable is assumed to be zero unless a
starting value was specified via SetStartingVariableValuesForNextSolve().

Note that, at the end of the solve, some of these FREE variable with bounds
and an interior point value might still be left in the final solution.
Enable push_to_vertex to clean these up.

optional double crossover_bound_snapping_distance = 64 [default = inf];

Returns: The crossoverBoundSnappingDistance.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8492 of file GlopParameters.java.

◆ getDefaultInstanceForType()

com.google.ortools.glop.GlopParameters com.google.ortools.glop.GlopParameters.Builder.getDefaultInstanceForType ( )

Definition at line 4042 of file GlopParameters.java.

◆ getDegenerateMinistepFactor()

double com.google.ortools.glop.GlopParameters.Builder.getDegenerateMinistepFactor ( )

During a degenerate iteration, the more conservative approach is to do a
step of length zero (while shifting the bound of the leaving variable).
That is, the variable values are unchanged for the primal simplex or the
reduced cost are unchanged for the dual simplex. However, instead of doing
a step of length zero, it seems to be better on degenerate problems to do a
small positive step. This is what is recommended in the EXPAND procedure
described in:
P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright. "A practical anti-
cycling procedure for linearly constrained optimization".
Mathematical Programming, 45:437&#92;u2013474, 1989.

Here, during a degenerate iteration we do a small positive step of this
factor times the primal (resp. dual) tolerance. In the primal simplex, this
may effectively push variable values (very slightly) further out of their
bounds (resp. reduced costs for the dual simplex).

Setting this to zero reverts to the more conservative approach of a zero
step during degenerate iterations.

optional double degenerate_ministep_factor = 42 [default = 0.01];

Returns: The degenerateMinistepFactor.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7758 of file GlopParameters.java.

◆ getDescriptor()

static final com.google.protobuf.Descriptors.Descriptor com.google.ortools.glop.GlopParameters.Builder.getDescriptor ( )

static

Definition at line 3947 of file GlopParameters.java.

◆ getDescriptorForType()

com.google.protobuf.Descriptors.Descriptor com.google.ortools.glop.GlopParameters.Builder.getDescriptorForType ( )

Definition at line 4037 of file GlopParameters.java.

◆ getDevexWeightsResetPeriod()

int com.google.ortools.glop.GlopParameters.Builder.getDevexWeightsResetPeriod ( )

Devex weights will be reset to 1.0 after that number of updates.

optional int32 devex_weights_reset_period = 33 [default = 150];

Returns: The devexWeightsResetPeriod.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7144 of file GlopParameters.java.

◆ getDropMagnitude()

double com.google.ortools.glop.GlopParameters.Builder.getDropMagnitude ( )

Value in the input LP lower than this will be ignored. This is similar to
drop_tolerance but more aggressive as this is used before scaling. This is
mainly here to avoid underflow and have simpler invariant in the code, like
a * b == 0 iff a or b is zero and things like this.

optional double drop_magnitude = 71 [default = 1e-30];

Returns: The dropMagnitude.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8790 of file GlopParameters.java.

◆ getDropTolerance()

double com.google.ortools.glop.GlopParameters.Builder.getDropTolerance ( )

In order to increase the sparsity of the manipulated vectors, floating
point values with a magnitude smaller than this parameter are set to zero
(only in some places). This parameter should be positive or zero.

optional double drop_tolerance = 52 [default = 1e-14];

Returns: The dropTolerance.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5746 of file GlopParameters.java.

◆ getDualFeasibilityTolerance()

double com.google.ortools.glop.GlopParameters.Builder.getDualFeasibilityTolerance ( )

Variables whose reduced costs have an absolute value smaller than this
tolerance are not considered as entering candidates. That is they do not
take part in deciding whether a solution is dual-feasible or not.

Note that this value can temporarily increase during the execution of the
algorithm if the estimated precision of the reduced costs is higher than
this tolerance. Note also that we scale the costs (in the presolve step) so
that the cost magnitude range contains one.

This is also known as the optimality tolerance in other solvers.

optional double dual_feasibility_tolerance = 11 [default = 1e-08];

Returns: The dualFeasibilityTolerance.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5368 of file GlopParameters.java.

◆ getDualizerThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getDualizerThreshold ( )

When solve_dual_problem is LET_SOLVER_DECIDE, take the dual if the number
of constraints of the problem is more than this threshold times the number
of variables.

optional double dualizer_threshold = 21 [default = 1.5];

Returns: The dualizerThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6240 of file GlopParameters.java.

◆ getDualPricePrioritizeNorm()

boolean com.google.ortools.glop.GlopParameters.Builder.getDualPricePrioritizeNorm ( )

On some problem like stp3d or pds-100 this makes a huge difference in
speed and number of iterations of the dual simplex.

optional bool dual_price_prioritize_norm = 69 [default = false];

Returns: The dualPricePrioritizeNorm.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8854 of file GlopParameters.java.

◆ getDualSmallPivotThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getDualSmallPivotThreshold ( )

Like small_pivot_threshold but for the dual simplex. This is needed because
the dual algorithm does not interpret this value in the same way.
TODO(user): Clean this up and use the same small pivot detection.

optional double dual_small_pivot_threshold = 38 [default = 0.0001];

Returns: The dualSmallPivotThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7460 of file GlopParameters.java.

◆ getDynamicallyAdjustRefactorizationPeriod()

boolean com.google.ortools.glop.GlopParameters.Builder.getDynamicallyAdjustRefactorizationPeriod ( )

If this is true, then basis_refactorization_period becomes a lower bound on
the number of iterations between two refactorization (provided there is no
numerical accuracy issues). Depending on the estimated time to refactorize
vs the extra time spend in each solves because of the LU update, we try to
balance the two times.

optional bool dynamically_adjust_refactorization_period = 63 [default = true];

Returns: The dynamicallyAdjustRefactorizationPeriod.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6106 of file GlopParameters.java.

◆ getExploitSingletonColumnInInitialBasis()

boolean com.google.ortools.glop.GlopParameters.Builder.getExploitSingletonColumnInInitialBasis ( )

Whether or not we exploit the singleton columns already present in the
problem when we create the initial basis.

optional bool exploit_singleton_column_in_initial_basis = 37 [default = true];

Returns: The exploitSingletonColumnInInitialBasis.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7398 of file GlopParameters.java.

◆ getFeasibilityRule()

com.google.ortools.glop.GlopParameters.PricingRule com.google.ortools.glop.GlopParameters.Builder.getFeasibilityRule ( )

PricingRule to use during the feasibility phase.

optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];

Returns: The feasibilityRule.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 4933 of file GlopParameters.java.

◆ getHarrisToleranceRatio()

double com.google.ortools.glop.GlopParameters.Builder.getHarrisToleranceRatio ( )

This impacts the ratio test and indicates by how much we allow a basic
variable value that we move to go out of bounds. The value should be in
[0.0, 1.0) and should be interpreted as a ratio of the
primal_feasibility_tolerance. Setting this to 0.0 basically disables the
Harris ratio test while setting this too close to 1.0 will make it
difficult to keep the variable values inside their bounds modulo the
primal_feasibility_tolerance.

Note that the same comment applies to the dual simplex ratio test. There,
we allow the reduced costs to be of an infeasible sign by as much as this
ratio times the dual_feasibility_tolerance.

optional double harris_tolerance_ratio = 13 [default = 0.5];

Returns: The harrisToleranceRatio.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5542 of file GlopParameters.java.

◆ getInitialBasis()

com.google.ortools.glop.GlopParameters.InitialBasisHeuristic com.google.ortools.glop.GlopParameters.Builder.getInitialBasis ( )

What heuristic is used to try to replace the fixed slack columns in the
initial basis of the primal simplex.

optional .operations_research.glop.GlopParameters.InitialBasisHeuristic initial_basis = 17 [default = TRIANGULAR];

Returns: The initialBasis.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5909 of file GlopParameters.java.

◆ getInitialConditionNumberThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getInitialConditionNumberThreshold ( )

If our upper bound on the condition number of the initial basis (from our
heurisitic or a warm start) is above this threshold, we revert to an all
slack basis.

optional double initial_condition_number_threshold = 59 [default = 1e+50];

Returns: The initialConditionNumberThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8284 of file GlopParameters.java.

◆ getInitializeDevexWithColumnNorms()

boolean com.google.ortools.glop.GlopParameters.Builder.getInitializeDevexWithColumnNorms ( )

Whether we initialize devex weights to 1.0 or to the norms of the matrix
columns.

optional bool initialize_devex_with_column_norms = 36 [default = true];

Returns: The initializeDevexWithColumnNorms.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7338 of file GlopParameters.java.

◆ getLogSearchProgress()

boolean com.google.ortools.glop.GlopParameters.Builder.getLogSearchProgress ( )

If true, logs the progress of a solve to LOG(INFO). Note that the same
messages can also be turned on by displaying logs at level 1 for the
relevant files.

optional bool log_search_progress = 61 [default = false];

Returns: The logSearchProgress.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8348 of file GlopParameters.java.

◆ getLogToStdout()

boolean com.google.ortools.glop.GlopParameters.Builder.getLogToStdout ( )

If true, logs will be displayed to stdout instead of using Google log info.

optional bool log_to_stdout = 66 [default = true];

Returns: The logToStdout.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8408 of file GlopParameters.java.

◆ getLuFactorizationPivotThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getLuFactorizationPivotThreshold ( )

Threshold for LU-factorization: for stability reasons, the magnitude of the
chosen pivot at a given step is guaranteed to be greater than this
threshold times the maximum magnitude of all the possible pivot choices in
the same column. The value must be in [0,1].

optional double lu_factorization_pivot_threshold = 25 [default = 0.01];

Returns: The luFactorizationPivotThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6642 of file GlopParameters.java.

◆ getMarkowitzSingularityThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getMarkowitzSingularityThreshold ( )

If a pivot magnitude is smaller than this during the Markowitz LU
factorization, then the matrix is assumed to be singular. Note that
this is an absolute threshold and is not relative to the other possible
pivots on the same column (see lu_factorization_pivot_threshold).

optional double markowitz_singularity_threshold = 30 [default = 1e-15];

Returns: The markowitzSingularityThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6958 of file GlopParameters.java.

◆ getMarkowitzZlatevParameter()

int com.google.ortools.glop.GlopParameters.Builder.getMarkowitzZlatevParameter ( )

How many columns do we look at in the Markowitz pivoting rule to find
a good pivot. See markowitz.h.

optional int32 markowitz_zlatev_parameter = 29 [default = 3];

Returns: The markowitzZlatevParameter.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6894 of file GlopParameters.java.

◆ getMaxDeterministicTime()

double com.google.ortools.glop.GlopParameters.Builder.getMaxDeterministicTime ( )

Maximum deterministic time allowed to solve a problem. The deterministic
time is more or less correlated to the running time, and its unit should
be around the second (at least on a Xeon(R) CPU E5-1650 v2 &#64; 3.50GHz).

TODO(user): Improve the correlation.

optional double max_deterministic_time = 45 [default = inf];

Returns: The maxDeterministicTime.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6768 of file GlopParameters.java.

◆ getMaxNumberOfIterations()

long com.google.ortools.glop.GlopParameters.Builder.getMaxNumberOfIterations ( )

Maximum number of simplex iterations to solve a problem.
A value of -1 means no limit.

optional int64 max_number_of_iterations = 27 [default = -1];

Returns: The maxNumberOfIterations.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6834 of file GlopParameters.java.

◆ getMaxNumberOfReoptimizations()

double com.google.ortools.glop.GlopParameters.Builder.getMaxNumberOfReoptimizations ( )

When the solution of phase II is imprecise, we re-run the phase II with the
opposite algorithm from that imprecise solution (i.e., if primal or dual
simplex was used, we use dual or primal simplex, respectively). We repeat
such re-optimization until the solution is precise, or we hit this limit.

optional double max_number_of_reoptimizations = 56 [default = 40];

Returns: The maxNumberOfReoptimizations.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6574 of file GlopParameters.java.

◆ getMaxTimeInSeconds()

double com.google.ortools.glop.GlopParameters.Builder.getMaxTimeInSeconds ( )

Maximum time allowed in seconds to solve a problem.

optional double max_time_in_seconds = 26 [default = inf];

Returns: The maxTimeInSeconds.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6704 of file GlopParameters.java.

◆ getMaxValidMagnitude()

double com.google.ortools.glop.GlopParameters.Builder.getMaxValidMagnitude ( )

Any finite values in the input LP must be below this threshold, otherwise
the model will be reported invalid. This is needed to avoid floating point
overflow when evaluating bounds * coeff for instance. In practice, users
shouldn't use super large values in an LP. With the default threshold, even
evaluating large constraint with variables at their bound shouldn't cause
any overflow.

optional double max_valid_magnitude = 70 [default = 1e+30];

Returns: The maxValidMagnitude.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8718 of file GlopParameters.java.

◆ getMinimumAcceptablePivot()

double com.google.ortools.glop.GlopParameters.Builder.getMinimumAcceptablePivot ( )

We never follow a basis change with a pivot under this threshold.

optional double minimum_acceptable_pivot = 15 [default = 1e-06];

Returns: The minimumAcceptablePivot.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5686 of file GlopParameters.java.

◆ getNumOmpThreads()

int com.google.ortools.glop.GlopParameters.Builder.getNumOmpThreads ( )

Number of threads in the OMP parallel sections. If left to 1, the code will
not create any OMP threads and will remain single-threaded.

optional int32 num_omp_threads = 44 [default = 1];

Returns: The numOmpThreads.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7962 of file GlopParameters.java.

◆ getObjectiveLowerLimit()

double com.google.ortools.glop.GlopParameters.Builder.getObjectiveLowerLimit ( )

The solver will stop as soon as it has proven that the objective is smaller
than objective_lower_limit or greater than objective_upper_limit. Depending
on the simplex algorithm (primal or dual) and the optimization direction,
note that only one bound will be used at the time.

Important: The solver does not add any tolerances to these values, and as
soon as the objective (as computed by the solver, so with some imprecision)
crosses one of these bounds (strictly), the search will stop. It is up to
the client to add any tolerance if needed.

optional double objective_lower_limit = 40 [default = -inf];

Returns: The objectiveLowerLimit.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7612 of file GlopParameters.java.

◆ getObjectiveUpperLimit()

double com.google.ortools.glop.GlopParameters.Builder.getObjectiveUpperLimit ( )

optional double objective_upper_limit = 41 [default = inf];

Returns: The objectiveUpperLimit.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7676 of file GlopParameters.java.

◆ getOptimizationRule()

com.google.ortools.glop.GlopParameters.PricingRule com.google.ortools.glop.GlopParameters.Builder.getOptimizationRule ( )

PricingRule to use during the optimization phase.

optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];

Returns: The optimizationRule.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 4991 of file GlopParameters.java.

◆ getPerturbCostsInDualSimplex()

boolean com.google.ortools.glop.GlopParameters.Builder.getPerturbCostsInDualSimplex ( )

When this is true, then the costs are randomly perturbed before the dual
simplex is even started. This has been shown to improve the dual simplex
performance. For a good reference, see Huangfu Q (2013) "High performance
simplex solver", Ph.D, dissertation, University of Edinburgh.

optional bool perturb_costs_in_dual_simplex = 53 [default = false];

Returns: The perturbCostsInDualSimplex.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8026 of file GlopParameters.java.

◆ getPreprocessorZeroTolerance()

double com.google.ortools.glop.GlopParameters.Builder.getPreprocessorZeroTolerance ( )

A floating point tolerance used by the preprocessors. This is used for
things like detecting if two columns/rows are proportional or if an
interval is empty.

Note that the preprocessors also use solution_feasibility_tolerance() to
detect if a problem is infeasible.

optional double preprocessor_zero_tolerance = 39 [default = 1e-09];

Returns: The preprocessorZeroTolerance.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7530 of file GlopParameters.java.

◆ getPrimalFeasibilityTolerance()

double com.google.ortools.glop.GlopParameters.Builder.getPrimalFeasibilityTolerance ( )

This tolerance indicates by how much we allow the variable values to go out
of bounds and still consider the current solution primal-feasible. We also
use the same tolerance for the error A.x - b. Note that the two errors are
closely related if A is scaled in such a way that the greatest coefficient
magnitude on each column is 1.0.

This is also simply called feasibility tolerance in other solvers.

optional double primal_feasibility_tolerance = 10 [default = 1e-08];

Returns: The primalFeasibilityTolerance.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5282 of file GlopParameters.java.

◆ getProvideStrongOptimalGuarantee()

boolean com.google.ortools.glop.GlopParameters.Builder.getProvideStrongOptimalGuarantee ( )

If true, then when the solver returns a solution with an OPTIMAL status,
we can guarantee that:
- The primal variable are in their bounds.
- The dual variable are in their bounds.
- If we modify each component of the right-hand side a bit and each
component of the objective function a bit, then the pair (primal values,
dual values) is an EXACT optimal solution of the perturbed problem.
- The modifications above are smaller than the associated tolerances as
defined in the comment for solution_feasibility_tolerance (*).

(*): This is the only place where the guarantee is not tight since we
compute the upper bounds with scalar product of the primal/dual
solution and the initial problem coefficients with only double precision.

Note that whether or not this option is true, we still check the
primal/dual infeasibility and objective gap. However if it is false, we
don't move the primal/dual values within their bounds and leave them
untouched.

optional bool provide_strong_optimal_guarantee = 24 [default = true];

Returns: The provideStrongOptimalGuarantee.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6418 of file GlopParameters.java.

◆ getPushToVertex()

boolean com.google.ortools.glop.GlopParameters.Builder.getPushToVertex ( )

If the optimization phases finishes with super-basic variables (i.e.,
variables that either 1) have bounds but are FREE in the basis, or 2) have
no bounds and are FREE in the basis at a nonzero value), then run a "push"
phase to push these variables to bounds, obtaining a vertex solution. Note
this situation can happen only if a starting value was specified via
SetStartingVariableValuesForNextSolve().

optional bool push_to_vertex = 65 [default = true];

Returns: The pushToVertex.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8586 of file GlopParameters.java.

◆ getRandomSeed()

int com.google.ortools.glop.GlopParameters.Builder.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.
Note that this may lead to a different solution, for example a different
optimal basis.

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.

Also note that the solver is fully deterministic: two runs of the same
binary, on the same machine, on the exact same data and with the same
parameters will go through the exact same iterations. If they hit a time
limit, they might of course yield different results because one will have
advanced farther than the other.

optional int32 random_seed = 43 [default = 1];

Returns: The randomSeed.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7876 of file GlopParameters.java.

◆ getRatioTestZeroThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getRatioTestZeroThreshold ( )

During the primal simplex (resp. dual simplex), the coefficients of the
direction (resp. update row) with a magnitude lower than this threshold are
not considered during the ratio test. This tolerance is related to the
precision at which a Solve() involving the basis matrix can be performed.

TODO(user): Automatically increase it when we detect that the precision
of the Solve() is worse than this.

optional double ratio_test_zero_threshold = 12 [default = 1e-09];

Returns: The ratioTestZeroThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5454 of file GlopParameters.java.

◆ getRecomputeEdgesNormThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getRecomputeEdgesNormThreshold ( )

Note that the threshold is a relative error on the actual norm (not the
squared one) and that edge norms are always greater than 1. Recomputing
norms is a really expensive operation and a large threshold is ok since
this doesn't impact directly the solution but just the entering variable
choice.

optional double recompute_edges_norm_threshold = 9 [default = 100];

Returns: The recomputeEdgesNormThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5206 of file GlopParameters.java.

◆ getRecomputeReducedCostsThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getRecomputeReducedCostsThreshold ( )

We estimate the accuracy of the iteratively computed reduced costs. If
it falls below this threshold, we reinitialize them from scratch. Note
that such an operation is pretty fast, so we can use a low threshold.
It is important to have a good accuracy here (better than the
dual_feasibility_tolerance below) to be sure of the sign of such a cost.

optional double recompute_reduced_costs_threshold = 8 [default = 1e-08];

Returns: The recomputeReducedCostsThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5134 of file GlopParameters.java.

◆ getRefactorizationThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getRefactorizationThreshold ( )

We estimate the factorization accuracy of B during each pivot by using
the fact that we can compute the pivot coefficient in two ways:
- From direction[leaving_row].
- From update_row[entering_column].
If the two values have a relative difference above this threshold, we
trigger a refactorization.

optional double refactorization_threshold = 6 [default = 1e-09];

Returns: The refactorizationThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5060 of file GlopParameters.java.

◆ getRelativeCostPerturbation()

double com.google.ortools.glop.GlopParameters.Builder.getRelativeCostPerturbation ( )

The magnitude of the cost perturbation is given by
RandomIn(1.0, 2.0) * (
relative_cost_perturbation * cost
+ relative_max_cost_perturbation * max_cost);

optional double relative_cost_perturbation = 54 [default = 1e-05];

Returns: The relativeCostPerturbation.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8178 of file GlopParameters.java.

◆ getRelativeMaxCostPerturbation()

double com.google.ortools.glop.GlopParameters.Builder.getRelativeMaxCostPerturbation ( )

optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Returns: The relativeMaxCostPerturbation.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8232 of file GlopParameters.java.

◆ getScalingMethod()

com.google.ortools.glop.GlopParameters.ScalingAlgorithm com.google.ortools.glop.GlopParameters.Builder.getScalingMethod ( )

optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Returns: The scalingMethod.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 4883 of file GlopParameters.java.

◆ getSmallPivotThreshold()

double com.google.ortools.glop.GlopParameters.Builder.getSmallPivotThreshold ( )

When we choose the leaving variable, we want to avoid small pivot because
they are the less precise and may cause numerical instabilities. For a
pivot under this threshold times the infinity norm of the direction, we try
various countermeasures in order to avoid using it.

optional double small_pivot_threshold = 14 [default = 1e-06];

Returns: The smallPivotThreshold.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5624 of file GlopParameters.java.

◆ getSolutionFeasibilityTolerance()

double com.google.ortools.glop.GlopParameters.Builder.getSolutionFeasibilityTolerance ( )

When the problem status is OPTIMAL, we check the optimality using this
relative tolerance and change the status to IMPRECISE if an issue is
detected.

The tolerance is "relative" in the sense that our thresholds are:
- tolerance * max(1.0, abs(bound)) for crossing a given bound.
- tolerance * max(1.0, abs(cost)) for an infeasible reduced cost.
- tolerance for an infeasible dual value.

optional double solution_feasibility_tolerance = 22 [default = 1e-06];

Returns: The solutionFeasibilityTolerance.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6314 of file GlopParameters.java.

◆ getSolveDualProblem()

com.google.ortools.glop.GlopParameters.SolverBehavior com.google.ortools.glop.GlopParameters.Builder.getSolveDualProblem ( )

Whether or not we solve the dual of the given problem.
With a value of auto, the algorithm decide which approach is probably the
fastest depending on the problem dimensions (see dualizer_threshold).

optional .operations_research.glop.GlopParameters.SolverBehavior solve_dual_problem = 20 [default = LET_SOLVER_DECIDE];

Returns: The solveDualProblem.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6173 of file GlopParameters.java.

◆ getUseDedicatedDualFeasibilityAlgorithm()

boolean com.google.ortools.glop.GlopParameters.Builder.getUseDedicatedDualFeasibilityAlgorithm ( )

We have two possible dual phase I algorithms. Both work on an LP that
minimize the sum of dual infeasiblities. One use dedicated code (when this
param is true), the other one use exactly the same code as the dual phase
II but on an auxiliary problem where the variable bounds of the original
problem are changed.

TODO(user): For now we have both, but ideally the non-dedicated version
will win since it is a lot less code to maintain.

optional bool use_dedicated_dual_feasibility_algorithm = 62 [default = true];

Returns: The useDedicatedDualFeasibilityAlgorithm.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8102 of file GlopParameters.java.

◆ getUseDualSimplex()

boolean com.google.ortools.glop.GlopParameters.Builder.getUseDualSimplex ( )

Whether or not we use the dual simplex algorithm instead of the primal.

optional bool use_dual_simplex = 31 [default = false];

Returns: The useDualSimplex.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7020 of file GlopParameters.java.

◆ getUseImpliedFreePreprocessor()

boolean com.google.ortools.glop.GlopParameters.Builder.getUseImpliedFreePreprocessor ( )

If presolve runs, include the pass that detects implied free variables.

optional bool use_implied_free_preprocessor = 67 [default = true];

Returns: The useImpliedFreePreprocessor.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8652 of file GlopParameters.java.

◆ getUseMiddleProductFormUpdate()

boolean com.google.ortools.glop.GlopParameters.Builder.getUseMiddleProductFormUpdate ( )

Whether or not to use the middle product form update rather than the
standard eta LU update. The middle form product update should be a lot more
efficient (close to the Forrest-Tomlin update, a bit slower but easier to
implement). See for more details:
Qi Huangfu, J. A. Julian Hall, "Novel update techniques for the revised
simplex method", 28 january 2013, Technical Report ERGO-13-0001
http://www.maths.ed.ac.uk/hall/HuHa12/ERGO-13-001.pdf

optional bool use_middle_product_form_update = 35 [default = true];

Returns: The useMiddleProductFormUpdate.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7268 of file GlopParameters.java.

◆ getUsePreprocessing()

boolean com.google.ortools.glop.GlopParameters.Builder.getUsePreprocessing ( )

Whether or not we use advanced preprocessing techniques.

optional bool use_preprocessing = 34 [default = true];

Returns: The usePreprocessing.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7200 of file GlopParameters.java.

◆ getUseScaling()

boolean com.google.ortools.glop.GlopParameters.Builder.getUseScaling ( )

Whether or not we scale the matrix A so that the maximum coefficient on
each line and each column is 1.0.

optional bool use_scaling = 16 [default = true];

Returns: The useScaling.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5808 of file GlopParameters.java.

◆ getUseTransposedMatrix()

boolean com.google.ortools.glop.GlopParameters.Builder.getUseTransposedMatrix ( )

Whether or not we keep a transposed version of the matrix A to speed-up the
pricing at the cost of extra memory and the initial tranposition
computation.

optional bool use_transposed_matrix = 18 [default = true];

Returns: The useTransposedMatrix.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5974 of file GlopParameters.java.

◆ hasAllowSimplexAlgorithmChange()

boolean com.google.ortools.glop.GlopParameters.Builder.hasAllowSimplexAlgorithmChange ( )

During incremental solve, let the solver decide if it use the primal or
dual simplex algorithm depending on the current solution and on the new
problem. Note that even if this is true, the value of use_dual_simplex
still indicates the default algorithm that the solver will use.

optional bool allow_simplex_algorithm_change = 32 [default = false];

Returns: Whether the allowSimplexAlgorithmChange field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7067 of file GlopParameters.java.

◆ hasBasisRefactorizationPeriod()

boolean com.google.ortools.glop.GlopParameters.Builder.hasBasisRefactorizationPeriod ( )

Number of iterations between two basis refactorizations. Note that various
conditions in the algorithm may trigger a refactorization before this
period is reached. Set this to 0 if you want to refactorize at each step.

optional int32 basis_refactorization_period = 19 [default = 64];

Returns: Whether the basisRefactorizationPeriod field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6024 of file GlopParameters.java.

◆ hasChangeStatusToImprecise()

boolean com.google.ortools.glop.GlopParameters.Builder.hasChangeStatusToImprecise ( )

If true, the internal API will change the return status to imprecise if the
solution does not respect the internal tolerances.

optional bool change_status_to_imprecise = 58 [default = true];

Returns: Whether the changeStatusToImprecise field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6497 of file GlopParameters.java.

◆ hasCostScaling()

boolean com.google.ortools.glop.GlopParameters.Builder.hasCostScaling ( )

optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Returns: Whether the costScaling field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5849 of file GlopParameters.java.

◆ hasCrossoverBoundSnappingDistance()

boolean com.google.ortools.glop.GlopParameters.Builder.hasCrossoverBoundSnappingDistance ( )

If the starting basis contains FREE variable with bounds, we will move
any such variable to their closer bounds if the distance is smaller than
this parameter.

The starting statuses can contains FREE variables with bounds, if a user
set it like this externally. Also, any variable with an initial BASIC
status that was not kept in the initial basis is marked as FREE before this
step is applied.

Note that by default a FREE variable is assumed to be zero unless a
starting value was specified via SetStartingVariableValuesForNextSolve().

Note that, at the end of the solve, some of these FREE variable with bounds
and an interior point value might still be left in the final solution.
Enable push_to_vertex to clean these up.

optional double crossover_bound_snapping_distance = 64 [default = inf];

Returns: Whether the crossoverBoundSnappingDistance field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8466 of file GlopParameters.java.

◆ hasDegenerateMinistepFactor()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDegenerateMinistepFactor ( )

During a degenerate iteration, the more conservative approach is to do a
step of length zero (while shifting the bound of the leaving variable).
That is, the variable values are unchanged for the primal simplex or the
reduced cost are unchanged for the dual simplex. However, instead of doing
a step of length zero, it seems to be better on degenerate problems to do a
small positive step. This is what is recommended in the EXPAND procedure
described in:
P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright. "A practical anti-
cycling procedure for linearly constrained optimization".
Mathematical Programming, 45:437&#92;u2013474, 1989.

Here, during a degenerate iteration we do a small positive step of this
factor times the primal (resp. dual) tolerance. In the primal simplex, this
may effectively push variable values (very slightly) further out of their
bounds (resp. reduced costs for the dual simplex).

Setting this to zero reverts to the more conservative approach of a zero
step during degenerate iterations.

optional double degenerate_ministep_factor = 42 [default = 0.01];

Returns: Whether the degenerateMinistepFactor field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7729 of file GlopParameters.java.

◆ hasDevexWeightsResetPeriod()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDevexWeightsResetPeriod ( )

Devex weights will be reset to 1.0 after that number of updates.

optional int32 devex_weights_reset_period = 33 [default = 150];

Returns: Whether the devexWeightsResetPeriod field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7132 of file GlopParameters.java.

◆ hasDropMagnitude()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDropMagnitude ( )

Value in the input LP lower than this will be ignored. This is similar to
drop_tolerance but more aggressive as this is used before scaling. This is
mainly here to avoid underflow and have simpler invariant in the code, like
a * b == 0 iff a or b is zero and things like this.

optional double drop_magnitude = 71 [default = 1e-30];

Returns: Whether the dropMagnitude field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8775 of file GlopParameters.java.

◆ hasDropTolerance()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDropTolerance ( )

In order to increase the sparsity of the manipulated vectors, floating
point values with a magnitude smaller than this parameter are set to zero
(only in some places). This parameter should be positive or zero.

optional double drop_tolerance = 52 [default = 1e-14];

Returns: Whether the dropTolerance field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5732 of file GlopParameters.java.

◆ hasDualFeasibilityTolerance()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDualFeasibilityTolerance ( )

Variables whose reduced costs have an absolute value smaller than this
tolerance are not considered as entering candidates. That is they do not
take part in deciding whether a solution is dual-feasible or not.

Note that this value can temporarily increase during the execution of the
algorithm if the estimated precision of the reduced costs is higher than
this tolerance. Note also that we scale the costs (in the presolve step) so
that the cost magnitude range contains one.

This is also known as the optimality tolerance in other solvers.

optional double dual_feasibility_tolerance = 11 [default = 1e-08];

Returns: Whether the dualFeasibilityTolerance field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5347 of file GlopParameters.java.

◆ hasDualizerThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDualizerThreshold ( )

When solve_dual_problem is LET_SOLVER_DECIDE, take the dual if the number
of constraints of the problem is more than this threshold times the number
of variables.

optional double dualizer_threshold = 21 [default = 1.5];

Returns: Whether the dualizerThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6226 of file GlopParameters.java.

◆ hasDualPricePrioritizeNorm()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDualPricePrioritizeNorm ( )

On some problem like stp3d or pds-100 this makes a huge difference in
speed and number of iterations of the dual simplex.

optional bool dual_price_prioritize_norm = 69 [default = false];

Returns: Whether the dualPricePrioritizeNorm field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8841 of file GlopParameters.java.

◆ hasDualSmallPivotThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDualSmallPivotThreshold ( )

Like small_pivot_threshold but for the dual simplex. This is needed because
the dual algorithm does not interpret this value in the same way.
TODO(user): Clean this up and use the same small pivot detection.

optional double dual_small_pivot_threshold = 38 [default = 0.0001];

Returns: Whether the dualSmallPivotThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7446 of file GlopParameters.java.

◆ hasDynamicallyAdjustRefactorizationPeriod()

boolean com.google.ortools.glop.GlopParameters.Builder.hasDynamicallyAdjustRefactorizationPeriod ( )

If this is true, then basis_refactorization_period becomes a lower bound on
the number of iterations between two refactorization (provided there is no
numerical accuracy issues). Depending on the estimated time to refactorize
vs the extra time spend in each solves because of the LU update, we try to
balance the two times.

optional bool dynamically_adjust_refactorization_period = 63 [default = true];

Returns: Whether the dynamicallyAdjustRefactorizationPeriod field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6090 of file GlopParameters.java.

◆ hasExploitSingletonColumnInInitialBasis()

boolean com.google.ortools.glop.GlopParameters.Builder.hasExploitSingletonColumnInInitialBasis ( )

Whether or not we exploit the singleton columns already present in the
problem when we create the initial basis.

optional bool exploit_singleton_column_in_initial_basis = 37 [default = true];

Returns: Whether the exploitSingletonColumnInInitialBasis field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7385 of file GlopParameters.java.

◆ hasFeasibilityRule()

boolean com.google.ortools.glop.GlopParameters.Builder.hasFeasibilityRule ( )

PricingRule to use during the feasibility phase.

optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];

Returns: Whether the feasibilityRule field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 4921 of file GlopParameters.java.

◆ hasHarrisToleranceRatio()

boolean com.google.ortools.glop.GlopParameters.Builder.hasHarrisToleranceRatio ( )

This impacts the ratio test and indicates by how much we allow a basic
variable value that we move to go out of bounds. The value should be in
[0.0, 1.0) and should be interpreted as a ratio of the
primal_feasibility_tolerance. Setting this to 0.0 basically disables the
Harris ratio test while setting this too close to 1.0 will make it
difficult to keep the variable values inside their bounds modulo the
primal_feasibility_tolerance.

Note that the same comment applies to the dual simplex ratio test. There,
we allow the reduced costs to be of an infeasible sign by as much as this
ratio times the dual_feasibility_tolerance.

optional double harris_tolerance_ratio = 13 [default = 0.5];

Returns: Whether the harrisToleranceRatio field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5520 of file GlopParameters.java.

◆ hasInitialBasis()

boolean com.google.ortools.glop.GlopParameters.Builder.hasInitialBasis ( )

What heuristic is used to try to replace the fixed slack columns in the
initial basis of the primal simplex.

optional .operations_research.glop.GlopParameters.InitialBasisHeuristic initial_basis = 17 [default = TRIANGULAR];

Returns: Whether the initialBasis field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5896 of file GlopParameters.java.

◆ hasInitialConditionNumberThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasInitialConditionNumberThreshold ( )

If our upper bound on the condition number of the initial basis (from our
heurisitic or a warm start) is above this threshold, we revert to an all
slack basis.

optional double initial_condition_number_threshold = 59 [default = 1e+50];

Returns: Whether the initialConditionNumberThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8270 of file GlopParameters.java.

◆ hasInitializeDevexWithColumnNorms()

boolean com.google.ortools.glop.GlopParameters.Builder.hasInitializeDevexWithColumnNorms ( )

Whether we initialize devex weights to 1.0 or to the norms of the matrix
columns.

optional bool initialize_devex_with_column_norms = 36 [default = true];

Returns: Whether the initializeDevexWithColumnNorms field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7325 of file GlopParameters.java.

◆ hasLogSearchProgress()

boolean com.google.ortools.glop.GlopParameters.Builder.hasLogSearchProgress ( )

If true, logs the progress of a solve to LOG(INFO). Note that the same
messages can also be turned on by displaying logs at level 1 for the
relevant files.

optional bool log_search_progress = 61 [default = false];

Returns: Whether the logSearchProgress field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8334 of file GlopParameters.java.

◆ hasLogToStdout()

boolean com.google.ortools.glop.GlopParameters.Builder.hasLogToStdout ( )

If true, logs will be displayed to stdout instead of using Google log info.

optional bool log_to_stdout = 66 [default = true];

Returns: Whether the logToStdout field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8396 of file GlopParameters.java.

◆ hasLuFactorizationPivotThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasLuFactorizationPivotThreshold ( )

Threshold for LU-factorization: for stability reasons, the magnitude of the
chosen pivot at a given step is guaranteed to be greater than this
threshold times the maximum magnitude of all the possible pivot choices in
the same column. The value must be in [0,1].

optional double lu_factorization_pivot_threshold = 25 [default = 0.01];

Returns: Whether the luFactorizationPivotThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6627 of file GlopParameters.java.

◆ hasMarkowitzSingularityThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMarkowitzSingularityThreshold ( )

If a pivot magnitude is smaller than this during the Markowitz LU
factorization, then the matrix is assumed to be singular. Note that
this is an absolute threshold and is not relative to the other possible
pivots on the same column (see lu_factorization_pivot_threshold).

optional double markowitz_singularity_threshold = 30 [default = 1e-15];

Returns: Whether the markowitzSingularityThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6943 of file GlopParameters.java.

◆ hasMarkowitzZlatevParameter()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMarkowitzZlatevParameter ( )

How many columns do we look at in the Markowitz pivoting rule to find
a good pivot. See markowitz.h.

optional int32 markowitz_zlatev_parameter = 29 [default = 3];

Returns: Whether the markowitzZlatevParameter field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6881 of file GlopParameters.java.

◆ hasMaxDeterministicTime()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMaxDeterministicTime ( )

Maximum deterministic time allowed to solve a problem. The deterministic
time is more or less correlated to the running time, and its unit should
be around the second (at least on a Xeon(R) CPU E5-1650 v2 &#64; 3.50GHz).

TODO(user): Improve the correlation.

optional double max_deterministic_time = 45 [default = inf];

Returns: Whether the maxDeterministicTime field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6752 of file GlopParameters.java.

◆ hasMaxNumberOfIterations()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMaxNumberOfIterations ( )

Maximum number of simplex iterations to solve a problem.
A value of -1 means no limit.

optional int64 max_number_of_iterations = 27 [default = -1];

Returns: Whether the maxNumberOfIterations field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6821 of file GlopParameters.java.

◆ hasMaxNumberOfReoptimizations()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMaxNumberOfReoptimizations ( )

When the solution of phase II is imprecise, we re-run the phase II with the
opposite algorithm from that imprecise solution (i.e., if primal or dual
simplex was used, we use dual or primal simplex, respectively). We repeat
such re-optimization until the solution is precise, or we hit this limit.

optional double max_number_of_reoptimizations = 56 [default = 40];

Returns: Whether the maxNumberOfReoptimizations field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6559 of file GlopParameters.java.

◆ hasMaxTimeInSeconds()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMaxTimeInSeconds ( )

Maximum time allowed in seconds to solve a problem.

optional double max_time_in_seconds = 26 [default = inf];

Returns: Whether the maxTimeInSeconds field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6692 of file GlopParameters.java.

◆ hasMaxValidMagnitude()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMaxValidMagnitude ( )

Any finite values in the input LP must be below this threshold, otherwise
the model will be reported invalid. This is needed to avoid floating point
overflow when evaluating bounds * coeff for instance. In practice, users
shouldn't use super large values in an LP. With the default threshold, even
evaluating large constraint with variables at their bound shouldn't cause
any overflow.

optional double max_valid_magnitude = 70 [default = 1e+30];

Returns: Whether the maxValidMagnitude field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8701 of file GlopParameters.java.

◆ hasMinimumAcceptablePivot()

boolean com.google.ortools.glop.GlopParameters.Builder.hasMinimumAcceptablePivot ( )

We never follow a basis change with a pivot under this threshold.

optional double minimum_acceptable_pivot = 15 [default = 1e-06];

Returns: Whether the minimumAcceptablePivot field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5674 of file GlopParameters.java.

◆ hasNumOmpThreads()

boolean com.google.ortools.glop.GlopParameters.Builder.hasNumOmpThreads ( )

Number of threads in the OMP parallel sections. If left to 1, the code will
not create any OMP threads and will remain single-threaded.

optional int32 num_omp_threads = 44 [default = 1];

Returns: Whether the numOmpThreads field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7949 of file GlopParameters.java.

◆ hasObjectiveLowerLimit()

boolean com.google.ortools.glop.GlopParameters.Builder.hasObjectiveLowerLimit ( )

The solver will stop as soon as it has proven that the objective is smaller
than objective_lower_limit or greater than objective_upper_limit. Depending
on the simplex algorithm (primal or dual) and the optimization direction,
note that only one bound will be used at the time.

Important: The solver does not add any tolerances to these values, and as
soon as the objective (as computed by the solver, so with some imprecision)
crosses one of these bounds (strictly), the search will stop. It is up to
the client to add any tolerance if needed.

optional double objective_lower_limit = 40 [default = -inf];

Returns: Whether the objectiveLowerLimit field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7592 of file GlopParameters.java.

◆ hasObjectiveUpperLimit()

boolean com.google.ortools.glop.GlopParameters.Builder.hasObjectiveUpperLimit ( )

optional double objective_upper_limit = 41 [default = inf];

Returns: Whether the objectiveUpperLimit field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7668 of file GlopParameters.java.

◆ hasOptimizationRule()

boolean com.google.ortools.glop.GlopParameters.Builder.hasOptimizationRule ( )

PricingRule to use during the optimization phase.

optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];

Returns: Whether the optimizationRule field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 4979 of file GlopParameters.java.

◆ hasPerturbCostsInDualSimplex()

boolean com.google.ortools.glop.GlopParameters.Builder.hasPerturbCostsInDualSimplex ( )

When this is true, then the costs are randomly perturbed before the dual
simplex is even started. This has been shown to improve the dual simplex
performance. For a good reference, see Huangfu Q (2013) "High performance
simplex solver", Ph.D, dissertation, University of Edinburgh.

optional bool perturb_costs_in_dual_simplex = 53 [default = false];

Returns: Whether the perturbCostsInDualSimplex field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8011 of file GlopParameters.java.

◆ hasPreprocessorZeroTolerance()

boolean com.google.ortools.glop.GlopParameters.Builder.hasPreprocessorZeroTolerance ( )

A floating point tolerance used by the preprocessors. This is used for
things like detecting if two columns/rows are proportional or if an
interval is empty.

Note that the preprocessors also use solution_feasibility_tolerance() to
detect if a problem is infeasible.

optional double preprocessor_zero_tolerance = 39 [default = 1e-09];

Returns: Whether the preprocessorZeroTolerance field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7513 of file GlopParameters.java.

◆ hasPrimalFeasibilityTolerance()

boolean com.google.ortools.glop.GlopParameters.Builder.hasPrimalFeasibilityTolerance ( )

This tolerance indicates by how much we allow the variable values to go out
of bounds and still consider the current solution primal-feasible. We also
use the same tolerance for the error A.x - b. Note that the two errors are
closely related if A is scaled in such a way that the greatest coefficient
magnitude on each column is 1.0.

This is also simply called feasibility tolerance in other solvers.

optional double primal_feasibility_tolerance = 10 [default = 1e-08];

Returns: Whether the primalFeasibilityTolerance field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5264 of file GlopParameters.java.

◆ hasProvideStrongOptimalGuarantee()

boolean com.google.ortools.glop.GlopParameters.Builder.hasProvideStrongOptimalGuarantee ( )

If true, then when the solver returns a solution with an OPTIMAL status,
we can guarantee that:
- The primal variable are in their bounds.
- The dual variable are in their bounds.
- If we modify each component of the right-hand side a bit and each
component of the objective function a bit, then the pair (primal values,
dual values) is an EXACT optimal solution of the perturbed problem.
- The modifications above are smaller than the associated tolerances as
defined in the comment for solution_feasibility_tolerance (*).

(*): This is the only place where the guarantee is not tight since we
compute the upper bounds with scalar product of the primal/dual
solution and the initial problem coefficients with only double precision.

Note that whether or not this option is true, we still check the
primal/dual infeasibility and objective gap. However if it is false, we
don't move the primal/dual values within their bounds and leave them
untouched.

optional bool provide_strong_optimal_guarantee = 24 [default = true];

Returns: Whether the provideStrongOptimalGuarantee field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6389 of file GlopParameters.java.

◆ hasPushToVertex()

boolean com.google.ortools.glop.GlopParameters.Builder.hasPushToVertex ( )

If the optimization phases finishes with super-basic variables (i.e.,
variables that either 1) have bounds but are FREE in the basis, or 2) have
no bounds and are FREE in the basis at a nonzero value), then run a "push"
phase to push these variables to bounds, obtaining a vertex solution. Note
this situation can happen only if a starting value was specified via
SetStartingVariableValuesForNextSolve().

optional bool push_to_vertex = 65 [default = true];

Returns: Whether the pushToVertex field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8569 of file GlopParameters.java.

◆ hasRandomSeed()

boolean com.google.ortools.glop.GlopParameters.Builder.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.
Note that this may lead to a different solution, for example a different
optimal basis.

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.

Also note that the solver is fully deterministic: two runs of the same
binary, on the same machine, on the exact same data and with the same
parameters will go through the exact same iterations. If they hit a time
limit, they might of course yield different results because one will have
advanced farther than the other.

optional int32 random_seed = 43 [default = 1];

Returns: Whether the randomSeed field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7850 of file GlopParameters.java.

◆ hasRatioTestZeroThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasRatioTestZeroThreshold ( )

During the primal simplex (resp. dual simplex), the coefficients of the
direction (resp. update row) with a magnitude lower than this threshold are
not considered during the ratio test. This tolerance is related to the
precision at which a Solve() involving the basis matrix can be performed.

TODO(user): Automatically increase it when we detect that the precision
of the Solve() is worse than this.

optional double ratio_test_zero_threshold = 12 [default = 1e-09];

Returns: Whether the ratioTestZeroThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5436 of file GlopParameters.java.

◆ hasRecomputeEdgesNormThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasRecomputeEdgesNormThreshold ( )

Note that the threshold is a relative error on the actual norm (not the
squared one) and that edge norms are always greater than 1. Recomputing
norms is a really expensive operation and a large threshold is ok since
this doesn't impact directly the solution but just the entering variable
choice.

optional double recompute_edges_norm_threshold = 9 [default = 100];

Returns: Whether the recomputeEdgesNormThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5190 of file GlopParameters.java.

◆ hasRecomputeReducedCostsThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasRecomputeReducedCostsThreshold ( )

We estimate the accuracy of the iteratively computed reduced costs. If
it falls below this threshold, we reinitialize them from scratch. Note
that such an operation is pretty fast, so we can use a low threshold.
It is important to have a good accuracy here (better than the
dual_feasibility_tolerance below) to be sure of the sign of such a cost.

optional double recompute_reduced_costs_threshold = 8 [default = 1e-08];

Returns: Whether the recomputeReducedCostsThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5118 of file GlopParameters.java.

◆ hasRefactorizationThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasRefactorizationThreshold ( )

We estimate the factorization accuracy of B during each pivot by using
the fact that we can compute the pivot coefficient in two ways:
- From direction[leaving_row].
- From update_row[entering_column].
If the two values have a relative difference above this threshold, we
trigger a refactorization.

optional double refactorization_threshold = 6 [default = 1e-09];

Returns: Whether the refactorizationThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5043 of file GlopParameters.java.

◆ hasRelativeCostPerturbation()

boolean com.google.ortools.glop.GlopParameters.Builder.hasRelativeCostPerturbation ( )

The magnitude of the cost perturbation is given by
RandomIn(1.0, 2.0) * (
relative_cost_perturbation * cost
+ relative_max_cost_perturbation * max_cost);

optional double relative_cost_perturbation = 54 [default = 1e-05];

Returns: Whether the relativeCostPerturbation field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8163 of file GlopParameters.java.

◆ hasRelativeMaxCostPerturbation()

boolean com.google.ortools.glop.GlopParameters.Builder.hasRelativeMaxCostPerturbation ( )

optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Returns: Whether the relativeMaxCostPerturbation field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8224 of file GlopParameters.java.

◆ hasScalingMethod()

boolean com.google.ortools.glop.GlopParameters.Builder.hasScalingMethod ( )

optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Returns: Whether the scalingMethod field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 4875 of file GlopParameters.java.

◆ hasSmallPivotThreshold()

boolean com.google.ortools.glop.GlopParameters.Builder.hasSmallPivotThreshold ( )

When we choose the leaving variable, we want to avoid small pivot because
they are the less precise and may cause numerical instabilities. For a
pivot under this threshold times the infinity norm of the direction, we try
various countermeasures in order to avoid using it.

optional double small_pivot_threshold = 14 [default = 1e-06];

Returns: Whether the smallPivotThreshold field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5609 of file GlopParameters.java.

◆ hasSolutionFeasibilityTolerance()

boolean com.google.ortools.glop.GlopParameters.Builder.hasSolutionFeasibilityTolerance ( )

When the problem status is OPTIMAL, we check the optimality using this
relative tolerance and change the status to IMPRECISE if an issue is
detected.

The tolerance is "relative" in the sense that our thresholds are:
- tolerance * max(1.0, abs(bound)) for crossing a given bound.
- tolerance * max(1.0, abs(cost)) for an infeasible reduced cost.
- tolerance for an infeasible dual value.

optional double solution_feasibility_tolerance = 22 [default = 1e-06];

Returns: Whether the solutionFeasibilityTolerance field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6295 of file GlopParameters.java.

◆ hasSolveDualProblem()

boolean com.google.ortools.glop.GlopParameters.Builder.hasSolveDualProblem ( )

Whether or not we solve the dual of the given problem.
With a value of auto, the algorithm decide which approach is probably the
fastest depending on the problem dimensions (see dualizer_threshold).

optional .operations_research.glop.GlopParameters.SolverBehavior solve_dual_problem = 20 [default = LET_SOLVER_DECIDE];

Returns: Whether the solveDualProblem field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 6159 of file GlopParameters.java.

◆ hasUseDedicatedDualFeasibilityAlgorithm()

boolean com.google.ortools.glop.GlopParameters.Builder.hasUseDedicatedDualFeasibilityAlgorithm ( )

We have two possible dual phase I algorithms. Both work on an LP that
minimize the sum of dual infeasiblities. One use dedicated code (when this
param is true), the other one use exactly the same code as the dual phase
II but on an auxiliary problem where the variable bounds of the original
problem are changed.

TODO(user): For now we have both, but ideally the non-dedicated version
will win since it is a lot less code to maintain.

optional bool use_dedicated_dual_feasibility_algorithm = 62 [default = true];

Returns: Whether the useDedicatedDualFeasibilityAlgorithm field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8083 of file GlopParameters.java.

◆ hasUseDualSimplex()

boolean com.google.ortools.glop.GlopParameters.Builder.hasUseDualSimplex ( )

Whether or not we use the dual simplex algorithm instead of the primal.

optional bool use_dual_simplex = 31 [default = false];

Returns: Whether the useDualSimplex field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7008 of file GlopParameters.java.

◆ hasUseImpliedFreePreprocessor()

boolean com.google.ortools.glop.GlopParameters.Builder.hasUseImpliedFreePreprocessor ( )

If presolve runs, include the pass that detects implied free variables.

optional bool use_implied_free_preprocessor = 67 [default = true];

Returns: Whether the useImpliedFreePreprocessor field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 8640 of file GlopParameters.java.

◆ hasUseMiddleProductFormUpdate()

boolean com.google.ortools.glop.GlopParameters.Builder.hasUseMiddleProductFormUpdate ( )

Whether or not to use the middle product form update rather than the
standard eta LU update. The middle form product update should be a lot more
efficient (close to the Forrest-Tomlin update, a bit slower but easier to
implement). See for more details:
Qi Huangfu, J. A. Julian Hall, "Novel update techniques for the revised
simplex method", 28 january 2013, Technical Report ERGO-13-0001
http://www.maths.ed.ac.uk/hall/HuHa12/ERGO-13-001.pdf

optional bool use_middle_product_form_update = 35 [default = true];

Returns: Whether the useMiddleProductFormUpdate field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7250 of file GlopParameters.java.

◆ hasUsePreprocessing()

boolean com.google.ortools.glop.GlopParameters.Builder.hasUsePreprocessing ( )

Whether or not we use advanced preprocessing techniques.

optional bool use_preprocessing = 34 [default = true];

Returns: Whether the usePreprocessing field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 7188 of file GlopParameters.java.

◆ hasUseScaling()

boolean com.google.ortools.glop.GlopParameters.Builder.hasUseScaling ( )

Whether or not we scale the matrix A so that the maximum coefficient on
each line and each column is 1.0.

optional bool use_scaling = 16 [default = true];

Returns: Whether the useScaling field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5795 of file GlopParameters.java.

◆ hasUseTransposedMatrix()

boolean com.google.ortools.glop.GlopParameters.Builder.hasUseTransposedMatrix ( )

Whether or not we keep a transposed version of the matrix A to speed-up the
pricing at the cost of extra memory and the initial tranposition
computation.

optional bool use_transposed_matrix = 18 [default = true];

Returns: Whether the useTransposedMatrix field is set.

Implements com.google.ortools.glop.GlopParametersOrBuilder.

Definition at line 5960 of file GlopParameters.java.

◆ internalGetFieldAccessorTable()

com.google.protobuf.GeneratedMessage.FieldAccessorTable com.google.ortools.glop.GlopParameters.Builder.internalGetFieldAccessorTable ( )

protected

Definition at line 3953 of file GlopParameters.java.

◆ isInitialized()

final boolean com.google.ortools.glop.GlopParameters.Builder.isInitialized ( )

Definition at line 4500 of file GlopParameters.java.

◆ mergeFrom() [1/3]

Builder com.google.ortools.glop.GlopParameters.Builder.mergeFrom ( com.google.ortools.glop.GlopParameters other )

Definition at line 4318 of file GlopParameters.java.

◆ mergeFrom() [2/3]

Builder com.google.ortools.glop.GlopParameters.Builder.mergeFrom	(	com.google.protobuf.CodedInputStream	input,
		com.google.protobuf.ExtensionRegistryLite	extensionRegistry ) throws java.io.IOException

Definition at line 4505 of file GlopParameters.java.

◆ mergeFrom() [3/3]

Builder com.google.ortools.glop.GlopParameters.Builder.mergeFrom ( com.google.protobuf.Message other )

Definition at line 4309 of file GlopParameters.java.

◆ setAllowSimplexAlgorithmChange()

Builder com.google.ortools.glop.GlopParameters.Builder.setAllowSimplexAlgorithmChange ( boolean value )

During incremental solve, let the solver decide if it use the primal or
dual simplex algorithm depending on the current solution and on the new
problem. Note that even if this is true, the value of use_dual_simplex
still indicates the default algorithm that the solver will use.

optional bool allow_simplex_algorithm_change = 32 [default = false];

Parameters

value The allowSimplexAlgorithmChange to set.

Returns: This builder for chaining.

Definition at line 7097 of file GlopParameters.java.

◆ setBasisRefactorizationPeriod()

Builder com.google.ortools.glop.GlopParameters.Builder.setBasisRefactorizationPeriod ( int value )

Number of iterations between two basis refactorizations. Note that various
conditions in the algorithm may trigger a refactorization before this
period is reached. Set this to 0 if you want to refactorize at each step.

optional int32 basis_refactorization_period = 19 [default = 64];

Parameters

value The basisRefactorizationPeriod to set.

Returns: This builder for chaining.

Definition at line 6052 of file GlopParameters.java.

◆ setChangeStatusToImprecise()

Builder com.google.ortools.glop.GlopParameters.Builder.setChangeStatusToImprecise ( boolean value )

If true, the internal API will change the return status to imprecise if the
solution does not respect the internal tolerances.

optional bool change_status_to_imprecise = 58 [default = true];

Parameters

value The changeStatusToImprecise to set.

Returns: This builder for chaining.

Definition at line 6523 of file GlopParameters.java.

◆ setCostScaling()

Builder com.google.ortools.glop.GlopParameters.Builder.setCostScaling ( com.google.ortools.glop.GlopParameters.CostScalingAlgorithm value )

optional .operations_research.glop.GlopParameters.CostScalingAlgorithm cost_scaling = 60 [default = CONTAIN_ONE_COST_SCALING];

Parameters

value The costScaling to set.

Returns: This builder for chaining.

Definition at line 5866 of file GlopParameters.java.

◆ setCrossoverBoundSnappingDistance()

Builder com.google.ortools.glop.GlopParameters.Builder.setCrossoverBoundSnappingDistance ( double value )

If the starting basis contains FREE variable with bounds, we will move
any such variable to their closer bounds if the distance is smaller than
this parameter.

The starting statuses can contains FREE variables with bounds, if a user
set it like this externally. Also, any variable with an initial BASIC
status that was not kept in the initial basis is marked as FREE before this
step is applied.

Note that by default a FREE variable is assumed to be zero unless a
starting value was specified via SetStartingVariableValuesForNextSolve().

Note that, at the end of the solve, some of these FREE variable with bounds
and an interior point value might still be left in the final solution.
Enable push_to_vertex to clean these up.

optional double crossover_bound_snapping_distance = 64 [default = inf];

Parameters

value The crossoverBoundSnappingDistance to set.

Returns: This builder for chaining.

Definition at line 8518 of file GlopParameters.java.

◆ setDegenerateMinistepFactor()

Builder com.google.ortools.glop.GlopParameters.Builder.setDegenerateMinistepFactor ( double value )

During a degenerate iteration, the more conservative approach is to do a
step of length zero (while shifting the bound of the leaving variable).
That is, the variable values are unchanged for the primal simplex or the
reduced cost are unchanged for the dual simplex. However, instead of doing
a step of length zero, it seems to be better on degenerate problems to do a
small positive step. This is what is recommended in the EXPAND procedure
described in:
P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright. "A practical anti-
cycling procedure for linearly constrained optimization".
Mathematical Programming, 45:437&#92;u2013474, 1989.

Here, during a degenerate iteration we do a small positive step of this
factor times the primal (resp. dual) tolerance. In the primal simplex, this
may effectively push variable values (very slightly) further out of their
bounds (resp. reduced costs for the dual simplex).

Setting this to zero reverts to the more conservative approach of a zero
step during degenerate iterations.

optional double degenerate_ministep_factor = 42 [default = 0.01];

Parameters

value The degenerateMinistepFactor to set.

Returns: This builder for chaining.

Definition at line 7787 of file GlopParameters.java.

◆ setDevexWeightsResetPeriod()

Builder com.google.ortools.glop.GlopParameters.Builder.setDevexWeightsResetPeriod ( int value )

Devex weights will be reset to 1.0 after that number of updates.

optional int32 devex_weights_reset_period = 33 [default = 150];

Parameters

value The devexWeightsResetPeriod to set.

Returns: This builder for chaining.

Definition at line 7156 of file GlopParameters.java.

◆ setDropMagnitude()

Builder com.google.ortools.glop.GlopParameters.Builder.setDropMagnitude ( double value )

Value in the input LP lower than this will be ignored. This is similar to
drop_tolerance but more aggressive as this is used before scaling. This is
mainly here to avoid underflow and have simpler invariant in the code, like
a * b == 0 iff a or b is zero and things like this.

optional double drop_magnitude = 71 [default = 1e-30];

Parameters

value The dropMagnitude to set.

Returns: This builder for chaining.

Definition at line 8805 of file GlopParameters.java.

◆ setDropTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.setDropTolerance ( double value )

In order to increase the sparsity of the manipulated vectors, floating
point values with a magnitude smaller than this parameter are set to zero
(only in some places). This parameter should be positive or zero.

optional double drop_tolerance = 52 [default = 1e-14];

Parameters

value The dropTolerance to set.

Returns: This builder for chaining.

Definition at line 5760 of file GlopParameters.java.

◆ setDualFeasibilityTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.setDualFeasibilityTolerance ( double value )

Variables whose reduced costs have an absolute value smaller than this
tolerance are not considered as entering candidates. That is they do not
take part in deciding whether a solution is dual-feasible or not.

Note that this value can temporarily increase during the execution of the
algorithm if the estimated precision of the reduced costs is higher than
this tolerance. Note also that we scale the costs (in the presolve step) so
that the cost magnitude range contains one.

This is also known as the optimality tolerance in other solvers.

optional double dual_feasibility_tolerance = 11 [default = 1e-08];

Parameters

value The dualFeasibilityTolerance to set.

Returns: This builder for chaining.

Definition at line 5389 of file GlopParameters.java.

◆ setDualizerThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setDualizerThreshold ( double value )

When solve_dual_problem is LET_SOLVER_DECIDE, take the dual if the number
of constraints of the problem is more than this threshold times the number
of variables.

optional double dualizer_threshold = 21 [default = 1.5];

Parameters

value The dualizerThreshold to set.

Returns: This builder for chaining.

Definition at line 6254 of file GlopParameters.java.

◆ setDualPricePrioritizeNorm()

Builder com.google.ortools.glop.GlopParameters.Builder.setDualPricePrioritizeNorm ( boolean value )

On some problem like stp3d or pds-100 this makes a huge difference in
speed and number of iterations of the dual simplex.

optional bool dual_price_prioritize_norm = 69 [default = false];

Parameters

value The dualPricePrioritizeNorm to set.

Returns: This builder for chaining.

Definition at line 8867 of file GlopParameters.java.

◆ setDualSmallPivotThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setDualSmallPivotThreshold ( double value )

Like small_pivot_threshold but for the dual simplex. This is needed because
the dual algorithm does not interpret this value in the same way.
TODO(user): Clean this up and use the same small pivot detection.

optional double dual_small_pivot_threshold = 38 [default = 0.0001];

Parameters

value The dualSmallPivotThreshold to set.

Returns: This builder for chaining.

Definition at line 7474 of file GlopParameters.java.

◆ setDynamicallyAdjustRefactorizationPeriod()

Builder com.google.ortools.glop.GlopParameters.Builder.setDynamicallyAdjustRefactorizationPeriod ( boolean value )

If this is true, then basis_refactorization_period becomes a lower bound on
the number of iterations between two refactorization (provided there is no
numerical accuracy issues). Depending on the estimated time to refactorize
vs the extra time spend in each solves because of the LU update, we try to
balance the two times.

optional bool dynamically_adjust_refactorization_period = 63 [default = true];

Parameters

value The dynamicallyAdjustRefactorizationPeriod to set.

Returns: This builder for chaining.

Definition at line 6122 of file GlopParameters.java.

◆ setExploitSingletonColumnInInitialBasis()

Builder com.google.ortools.glop.GlopParameters.Builder.setExploitSingletonColumnInInitialBasis ( boolean value )

Whether or not we exploit the singleton columns already present in the
problem when we create the initial basis.

optional bool exploit_singleton_column_in_initial_basis = 37 [default = true];

Parameters

value The exploitSingletonColumnInInitialBasis to set.

Returns: This builder for chaining.

Definition at line 7411 of file GlopParameters.java.

◆ setFeasibilityRule()

Builder com.google.ortools.glop.GlopParameters.Builder.setFeasibilityRule ( com.google.ortools.glop.GlopParameters.PricingRule value )

PricingRule to use during the feasibility phase.

optional .operations_research.glop.GlopParameters.PricingRule feasibility_rule = 1 [default = STEEPEST_EDGE];

Parameters

value The feasibilityRule to set.

Returns: This builder for chaining.

Definition at line 4946 of file GlopParameters.java.

◆ setHarrisToleranceRatio()

Builder com.google.ortools.glop.GlopParameters.Builder.setHarrisToleranceRatio ( double value )

This impacts the ratio test and indicates by how much we allow a basic
variable value that we move to go out of bounds. The value should be in
[0.0, 1.0) and should be interpreted as a ratio of the
primal_feasibility_tolerance. Setting this to 0.0 basically disables the
Harris ratio test while setting this too close to 1.0 will make it
difficult to keep the variable values inside their bounds modulo the
primal_feasibility_tolerance.

Note that the same comment applies to the dual simplex ratio test. There,
we allow the reduced costs to be of an infeasible sign by as much as this
ratio times the dual_feasibility_tolerance.

optional double harris_tolerance_ratio = 13 [default = 0.5];

Parameters

value The harrisToleranceRatio to set.

Returns: This builder for chaining.

Definition at line 5564 of file GlopParameters.java.

◆ setInitialBasis()

Builder com.google.ortools.glop.GlopParameters.Builder.setInitialBasis ( com.google.ortools.glop.GlopParameters.InitialBasisHeuristic value )

What heuristic is used to try to replace the fixed slack columns in the
initial basis of the primal simplex.

optional .operations_research.glop.GlopParameters.InitialBasisHeuristic initial_basis = 17 [default = TRIANGULAR];

Parameters

value The initialBasis to set.

Returns: This builder for chaining.

Definition at line 5923 of file GlopParameters.java.

◆ setInitialConditionNumberThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setInitialConditionNumberThreshold ( double value )

If our upper bound on the condition number of the initial basis (from our
heurisitic or a warm start) is above this threshold, we revert to an all
slack basis.

optional double initial_condition_number_threshold = 59 [default = 1e+50];

Parameters

value The initialConditionNumberThreshold to set.

Returns: This builder for chaining.

Definition at line 8298 of file GlopParameters.java.

◆ setInitializeDevexWithColumnNorms()

Builder com.google.ortools.glop.GlopParameters.Builder.setInitializeDevexWithColumnNorms ( boolean value )

Whether we initialize devex weights to 1.0 or to the norms of the matrix
columns.

optional bool initialize_devex_with_column_norms = 36 [default = true];

Parameters

value The initializeDevexWithColumnNorms to set.

Returns: This builder for chaining.

Definition at line 7351 of file GlopParameters.java.

◆ setLogSearchProgress()

Builder com.google.ortools.glop.GlopParameters.Builder.setLogSearchProgress ( boolean value )

If true, logs the progress of a solve to LOG(INFO). Note that the same
messages can also be turned on by displaying logs at level 1 for the
relevant files.

optional bool log_search_progress = 61 [default = false];

Parameters

value The logSearchProgress to set.

Returns: This builder for chaining.

Definition at line 8362 of file GlopParameters.java.

◆ setLogToStdout()

Builder com.google.ortools.glop.GlopParameters.Builder.setLogToStdout ( boolean value )

If true, logs will be displayed to stdout instead of using Google log info.

optional bool log_to_stdout = 66 [default = true];

Parameters

value The logToStdout to set.

Returns: This builder for chaining.

Definition at line 8420 of file GlopParameters.java.

◆ setLuFactorizationPivotThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setLuFactorizationPivotThreshold ( double value )

Threshold for LU-factorization: for stability reasons, the magnitude of the
chosen pivot at a given step is guaranteed to be greater than this
threshold times the maximum magnitude of all the possible pivot choices in
the same column. The value must be in [0,1].

optional double lu_factorization_pivot_threshold = 25 [default = 0.01];

Parameters

value The luFactorizationPivotThreshold to set.

Returns: This builder for chaining.

Definition at line 6657 of file GlopParameters.java.

◆ setMarkowitzSingularityThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setMarkowitzSingularityThreshold ( double value )

If a pivot magnitude is smaller than this during the Markowitz LU
factorization, then the matrix is assumed to be singular. Note that
this is an absolute threshold and is not relative to the other possible
pivots on the same column (see lu_factorization_pivot_threshold).

optional double markowitz_singularity_threshold = 30 [default = 1e-15];

Parameters

value The markowitzSingularityThreshold to set.

Returns: This builder for chaining.

Definition at line 6973 of file GlopParameters.java.

◆ setMarkowitzZlatevParameter()

Builder com.google.ortools.glop.GlopParameters.Builder.setMarkowitzZlatevParameter ( int value )

How many columns do we look at in the Markowitz pivoting rule to find
a good pivot. See markowitz.h.

optional int32 markowitz_zlatev_parameter = 29 [default = 3];

Parameters

value The markowitzZlatevParameter to set.

Returns: This builder for chaining.

Definition at line 6907 of file GlopParameters.java.

◆ setMaxDeterministicTime()

Builder com.google.ortools.glop.GlopParameters.Builder.setMaxDeterministicTime ( double value )

Maximum deterministic time allowed to solve a problem. The deterministic
time is more or less correlated to the running time, and its unit should
be around the second (at least on a Xeon(R) CPU E5-1650 v2 &#64; 3.50GHz).

TODO(user): Improve the correlation.

optional double max_deterministic_time = 45 [default = inf];

Parameters

value The maxDeterministicTime to set.

Returns: This builder for chaining.

Definition at line 6784 of file GlopParameters.java.

◆ setMaxNumberOfIterations()

Builder com.google.ortools.glop.GlopParameters.Builder.setMaxNumberOfIterations ( long value )

Maximum number of simplex iterations to solve a problem.
A value of -1 means no limit.

optional int64 max_number_of_iterations = 27 [default = -1];

Parameters

value The maxNumberOfIterations to set.

Returns: This builder for chaining.

Definition at line 6847 of file GlopParameters.java.

◆ setMaxNumberOfReoptimizations()

Builder com.google.ortools.glop.GlopParameters.Builder.setMaxNumberOfReoptimizations ( double value )

When the solution of phase II is imprecise, we re-run the phase II with the
opposite algorithm from that imprecise solution (i.e., if primal or dual
simplex was used, we use dual or primal simplex, respectively). We repeat
such re-optimization until the solution is precise, or we hit this limit.

optional double max_number_of_reoptimizations = 56 [default = 40];

Parameters

value The maxNumberOfReoptimizations to set.

Returns: This builder for chaining.

Definition at line 6589 of file GlopParameters.java.

◆ setMaxTimeInSeconds()

Builder com.google.ortools.glop.GlopParameters.Builder.setMaxTimeInSeconds ( double value )

Maximum time allowed in seconds to solve a problem.

optional double max_time_in_seconds = 26 [default = inf];

Parameters

value The maxTimeInSeconds to set.

Returns: This builder for chaining.

Definition at line 6716 of file GlopParameters.java.

◆ setMaxValidMagnitude()

Builder com.google.ortools.glop.GlopParameters.Builder.setMaxValidMagnitude ( double value )

Any finite values in the input LP must be below this threshold, otherwise
the model will be reported invalid. This is needed to avoid floating point
overflow when evaluating bounds * coeff for instance. In practice, users
shouldn't use super large values in an LP. With the default threshold, even
evaluating large constraint with variables at their bound shouldn't cause
any overflow.

optional double max_valid_magnitude = 70 [default = 1e+30];

Parameters

value The maxValidMagnitude to set.

Returns: This builder for chaining.

Definition at line 8735 of file GlopParameters.java.

◆ setMinimumAcceptablePivot()

Builder com.google.ortools.glop.GlopParameters.Builder.setMinimumAcceptablePivot ( double value )

We never follow a basis change with a pivot under this threshold.

optional double minimum_acceptable_pivot = 15 [default = 1e-06];

Parameters

value The minimumAcceptablePivot to set.

Returns: This builder for chaining.

Definition at line 5698 of file GlopParameters.java.

◆ setNumOmpThreads()

Builder com.google.ortools.glop.GlopParameters.Builder.setNumOmpThreads ( int value )

Number of threads in the OMP parallel sections. If left to 1, the code will
not create any OMP threads and will remain single-threaded.

optional int32 num_omp_threads = 44 [default = 1];

Parameters

value The numOmpThreads to set.

Returns: This builder for chaining.

Definition at line 7975 of file GlopParameters.java.

◆ setObjectiveLowerLimit()

Builder com.google.ortools.glop.GlopParameters.Builder.setObjectiveLowerLimit ( double value )

The solver will stop as soon as it has proven that the objective is smaller
than objective_lower_limit or greater than objective_upper_limit. Depending
on the simplex algorithm (primal or dual) and the optimization direction,
note that only one bound will be used at the time.

Important: The solver does not add any tolerances to these values, and as
soon as the objective (as computed by the solver, so with some imprecision)
crosses one of these bounds (strictly), the search will stop. It is up to
the client to add any tolerance if needed.

optional double objective_lower_limit = 40 [default = -inf];

Parameters

value The objectiveLowerLimit to set.

Returns: This builder for chaining.

Definition at line 7632 of file GlopParameters.java.

◆ setObjectiveUpperLimit()

Builder com.google.ortools.glop.GlopParameters.Builder.setObjectiveUpperLimit ( double value )

optional double objective_upper_limit = 41 [default = inf];

Parameters

value The objectiveUpperLimit to set.

Returns: This builder for chaining.

Definition at line 7684 of file GlopParameters.java.

◆ setOptimizationRule()

Builder com.google.ortools.glop.GlopParameters.Builder.setOptimizationRule ( com.google.ortools.glop.GlopParameters.PricingRule value )

PricingRule to use during the optimization phase.

optional .operations_research.glop.GlopParameters.PricingRule optimization_rule = 2 [default = STEEPEST_EDGE];

Parameters

value The optimizationRule to set.

Returns: This builder for chaining.

Definition at line 5004 of file GlopParameters.java.

◆ setPerturbCostsInDualSimplex()

Builder com.google.ortools.glop.GlopParameters.Builder.setPerturbCostsInDualSimplex ( boolean value )

When this is true, then the costs are randomly perturbed before the dual
simplex is even started. This has been shown to improve the dual simplex
performance. For a good reference, see Huangfu Q (2013) "High performance
simplex solver", Ph.D, dissertation, University of Edinburgh.

optional bool perturb_costs_in_dual_simplex = 53 [default = false];

Parameters

value The perturbCostsInDualSimplex to set.

Returns: This builder for chaining.

Definition at line 8041 of file GlopParameters.java.

◆ setPreprocessorZeroTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.setPreprocessorZeroTolerance ( double value )

A floating point tolerance used by the preprocessors. This is used for
things like detecting if two columns/rows are proportional or if an
interval is empty.

Note that the preprocessors also use solution_feasibility_tolerance() to
detect if a problem is infeasible.

optional double preprocessor_zero_tolerance = 39 [default = 1e-09];

Parameters

value The preprocessorZeroTolerance to set.

Returns: This builder for chaining.

Definition at line 7547 of file GlopParameters.java.

◆ setPrimalFeasibilityTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.setPrimalFeasibilityTolerance ( double value )

This tolerance indicates by how much we allow the variable values to go out
of bounds and still consider the current solution primal-feasible. We also
use the same tolerance for the error A.x - b. Note that the two errors are
closely related if A is scaled in such a way that the greatest coefficient
magnitude on each column is 1.0.

This is also simply called feasibility tolerance in other solvers.

optional double primal_feasibility_tolerance = 10 [default = 1e-08];

Parameters

value The primalFeasibilityTolerance to set.

Returns: This builder for chaining.

Definition at line 5300 of file GlopParameters.java.

◆ setProvideStrongOptimalGuarantee()

Builder com.google.ortools.glop.GlopParameters.Builder.setProvideStrongOptimalGuarantee ( boolean value )

If true, then when the solver returns a solution with an OPTIMAL status,
we can guarantee that:
- The primal variable are in their bounds.
- The dual variable are in their bounds.
- If we modify each component of the right-hand side a bit and each
component of the objective function a bit, then the pair (primal values,
dual values) is an EXACT optimal solution of the perturbed problem.
- The modifications above are smaller than the associated tolerances as
defined in the comment for solution_feasibility_tolerance (*).

(*): This is the only place where the guarantee is not tight since we
compute the upper bounds with scalar product of the primal/dual
solution and the initial problem coefficients with only double precision.

Note that whether or not this option is true, we still check the
primal/dual infeasibility and objective gap. However if it is false, we
don't move the primal/dual values within their bounds and leave them
untouched.

optional bool provide_strong_optimal_guarantee = 24 [default = true];

Parameters

value The provideStrongOptimalGuarantee to set.

Returns: This builder for chaining.

Definition at line 6447 of file GlopParameters.java.

◆ setPushToVertex()

Builder com.google.ortools.glop.GlopParameters.Builder.setPushToVertex ( boolean value )

If the optimization phases finishes with super-basic variables (i.e.,
variables that either 1) have bounds but are FREE in the basis, or 2) have
no bounds and are FREE in the basis at a nonzero value), then run a "push"
phase to push these variables to bounds, obtaining a vertex solution. Note
this situation can happen only if a starting value was specified via
SetStartingVariableValuesForNextSolve().

optional bool push_to_vertex = 65 [default = true];

Parameters

value The pushToVertex to set.

Returns: This builder for chaining.

Definition at line 8603 of file GlopParameters.java.

◆ setRandomSeed()

Builder com.google.ortools.glop.GlopParameters.Builder.setRandomSeed ( int value )

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.
Note that this may lead to a different solution, for example a different
optimal basis.

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.

Also note that the solver is fully deterministic: two runs of the same
binary, on the same machine, on the exact same data and with the same
parameters will go through the exact same iterations. If they hit a time
limit, they might of course yield different results because one will have
advanced farther than the other.

optional int32 random_seed = 43 [default = 1];

Parameters

value The randomSeed to set.

Returns: This builder for chaining.

Definition at line 7902 of file GlopParameters.java.

◆ setRatioTestZeroThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setRatioTestZeroThreshold ( double value )

During the primal simplex (resp. dual simplex), the coefficients of the
direction (resp. update row) with a magnitude lower than this threshold are
not considered during the ratio test. This tolerance is related to the
precision at which a Solve() involving the basis matrix can be performed.

TODO(user): Automatically increase it when we detect that the precision
of the Solve() is worse than this.

optional double ratio_test_zero_threshold = 12 [default = 1e-09];

Parameters

value The ratioTestZeroThreshold to set.

Returns: This builder for chaining.

Definition at line 5472 of file GlopParameters.java.

◆ setRecomputeEdgesNormThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setRecomputeEdgesNormThreshold ( double value )

Note that the threshold is a relative error on the actual norm (not the
squared one) and that edge norms are always greater than 1. Recomputing
norms is a really expensive operation and a large threshold is ok since
this doesn't impact directly the solution but just the entering variable
choice.

optional double recompute_edges_norm_threshold = 9 [default = 100];

Parameters

value The recomputeEdgesNormThreshold to set.

Returns: This builder for chaining.

Definition at line 5222 of file GlopParameters.java.

◆ setRecomputeReducedCostsThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setRecomputeReducedCostsThreshold ( double value )

We estimate the accuracy of the iteratively computed reduced costs. If
it falls below this threshold, we reinitialize them from scratch. Note
that such an operation is pretty fast, so we can use a low threshold.
It is important to have a good accuracy here (better than the
dual_feasibility_tolerance below) to be sure of the sign of such a cost.

optional double recompute_reduced_costs_threshold = 8 [default = 1e-08];

Parameters

value The recomputeReducedCostsThreshold to set.

Returns: This builder for chaining.

Definition at line 5150 of file GlopParameters.java.

◆ setRefactorizationThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setRefactorizationThreshold ( double value )

We estimate the factorization accuracy of B during each pivot by using
the fact that we can compute the pivot coefficient in two ways:
- From direction[leaving_row].
- From update_row[entering_column].
If the two values have a relative difference above this threshold, we
trigger a refactorization.

optional double refactorization_threshold = 6 [default = 1e-09];

Parameters

value The refactorizationThreshold to set.

Returns: This builder for chaining.

Definition at line 5077 of file GlopParameters.java.

◆ setRelativeCostPerturbation()

Builder com.google.ortools.glop.GlopParameters.Builder.setRelativeCostPerturbation ( double value )

The magnitude of the cost perturbation is given by
RandomIn(1.0, 2.0) * (
relative_cost_perturbation * cost
+ relative_max_cost_perturbation * max_cost);

optional double relative_cost_perturbation = 54 [default = 1e-05];

Parameters

value The relativeCostPerturbation to set.

Returns: This builder for chaining.

Definition at line 8193 of file GlopParameters.java.

◆ setRelativeMaxCostPerturbation()

Builder com.google.ortools.glop.GlopParameters.Builder.setRelativeMaxCostPerturbation ( double value )

optional double relative_max_cost_perturbation = 55 [default = 1e-07];

Parameters

value The relativeMaxCostPerturbation to set.

Returns: This builder for chaining.

Definition at line 8240 of file GlopParameters.java.

◆ setScalingMethod()

Builder com.google.ortools.glop.GlopParameters.Builder.setScalingMethod ( com.google.ortools.glop.GlopParameters.ScalingAlgorithm value )

optional .operations_research.glop.GlopParameters.ScalingAlgorithm scaling_method = 57 [default = EQUILIBRATION];

Parameters

value The scalingMethod to set.

Returns: This builder for chaining.

Definition at line 4892 of file GlopParameters.java.

◆ setSmallPivotThreshold()

Builder com.google.ortools.glop.GlopParameters.Builder.setSmallPivotThreshold ( double value )

When we choose the leaving variable, we want to avoid small pivot because
they are the less precise and may cause numerical instabilities. For a
pivot under this threshold times the infinity norm of the direction, we try
various countermeasures in order to avoid using it.

optional double small_pivot_threshold = 14 [default = 1e-06];

Parameters

value The smallPivotThreshold to set.

Returns: This builder for chaining.

Definition at line 5639 of file GlopParameters.java.

◆ setSolutionFeasibilityTolerance()

Builder com.google.ortools.glop.GlopParameters.Builder.setSolutionFeasibilityTolerance ( double value )

When the problem status is OPTIMAL, we check the optimality using this
relative tolerance and change the status to IMPRECISE if an issue is
detected.

The tolerance is "relative" in the sense that our thresholds are:
- tolerance * max(1.0, abs(bound)) for crossing a given bound.
- tolerance * max(1.0, abs(cost)) for an infeasible reduced cost.
- tolerance for an infeasible dual value.

optional double solution_feasibility_tolerance = 22 [default = 1e-06];

Parameters

value The solutionFeasibilityTolerance to set.

Returns: This builder for chaining.

Definition at line 6333 of file GlopParameters.java.

◆ setSolveDualProblem()

Builder com.google.ortools.glop.GlopParameters.Builder.setSolveDualProblem ( com.google.ortools.glop.GlopParameters.SolverBehavior value )

Whether or not we solve the dual of the given problem.
With a value of auto, the algorithm decide which approach is probably the
fastest depending on the problem dimensions (see dualizer_threshold).

optional .operations_research.glop.GlopParameters.SolverBehavior solve_dual_problem = 20 [default = LET_SOLVER_DECIDE];

Parameters

value The solveDualProblem to set.

Returns: This builder for chaining.

Definition at line 6188 of file GlopParameters.java.

◆ setUseDedicatedDualFeasibilityAlgorithm()

Builder com.google.ortools.glop.GlopParameters.Builder.setUseDedicatedDualFeasibilityAlgorithm ( boolean value )

We have two possible dual phase I algorithms. Both work on an LP that
minimize the sum of dual infeasiblities. One use dedicated code (when this
param is true), the other one use exactly the same code as the dual phase
II but on an auxiliary problem where the variable bounds of the original
problem are changed.

TODO(user): For now we have both, but ideally the non-dedicated version
will win since it is a lot less code to maintain.

optional bool use_dedicated_dual_feasibility_algorithm = 62 [default = true];

Parameters

value The useDedicatedDualFeasibilityAlgorithm to set.

Returns: This builder for chaining.

Definition at line 8121 of file GlopParameters.java.

◆ setUseDualSimplex()

Builder com.google.ortools.glop.GlopParameters.Builder.setUseDualSimplex ( boolean value )

Whether or not we use the dual simplex algorithm instead of the primal.

optional bool use_dual_simplex = 31 [default = false];

Parameters

value The useDualSimplex to set.

Returns: This builder for chaining.

Definition at line 7032 of file GlopParameters.java.

◆ setUseImpliedFreePreprocessor()

Builder com.google.ortools.glop.GlopParameters.Builder.setUseImpliedFreePreprocessor ( boolean value )

If presolve runs, include the pass that detects implied free variables.

optional bool use_implied_free_preprocessor = 67 [default = true];

Parameters

value The useImpliedFreePreprocessor to set.

Returns: This builder for chaining.

Definition at line 8664 of file GlopParameters.java.

◆ setUseMiddleProductFormUpdate()

Builder com.google.ortools.glop.GlopParameters.Builder.setUseMiddleProductFormUpdate ( boolean value )

Whether or not to use the middle product form update rather than the
standard eta LU update. The middle form product update should be a lot more
efficient (close to the Forrest-Tomlin update, a bit slower but easier to
implement). See for more details:
Qi Huangfu, J. A. Julian Hall, "Novel update techniques for the revised
simplex method", 28 january 2013, Technical Report ERGO-13-0001
http://www.maths.ed.ac.uk/hall/HuHa12/ERGO-13-001.pdf

optional bool use_middle_product_form_update = 35 [default = true];

Parameters

value The useMiddleProductFormUpdate to set.

Returns: This builder for chaining.

Definition at line 7286 of file GlopParameters.java.

◆ setUsePreprocessing()

Builder com.google.ortools.glop.GlopParameters.Builder.setUsePreprocessing ( boolean value )

Whether or not we use advanced preprocessing techniques.

optional bool use_preprocessing = 34 [default = true];

Parameters

value The usePreprocessing to set.

Returns: This builder for chaining.

Definition at line 7212 of file GlopParameters.java.

◆ setUseScaling()

Builder com.google.ortools.glop.GlopParameters.Builder.setUseScaling ( boolean value )

Whether or not we scale the matrix A so that the maximum coefficient on
each line and each column is 1.0.

optional bool use_scaling = 16 [default = true];

Parameters

value The useScaling to set.

Returns: This builder for chaining.

Definition at line 5821 of file GlopParameters.java.

◆ setUseTransposedMatrix()

Builder com.google.ortools.glop.GlopParameters.Builder.setUseTransposedMatrix ( boolean value )

Whether or not we keep a transposed version of the matrix A to speed-up the
pricing at the cost of extra memory and the initial tranposition
computation.

optional bool use_transposed_matrix = 18 [default = true];

Parameters

value The useTransposedMatrix to set.

Returns: This builder for chaining.

Definition at line 5988 of file GlopParameters.java.

The documentation for this class was generated from the following file:

build/java/ortools-java/src/main/java/com/google/ortools/glop/GlopParameters.java

Public Member Functions

Static Public Member Functions

Protected Member Functions

Detailed Description

Member Function Documentation

◆ build()

◆ buildPartial()

◆ clear()

◆ clearAllowSimplexAlgorithmChange()

◆ clearBasisRefactorizationPeriod()

◆ clearChangeStatusToImprecise()

◆ clearCostScaling()

◆ clearCrossoverBoundSnappingDistance()

◆ clearDegenerateMinistepFactor()

◆ clearDevexWeightsResetPeriod()

◆ clearDropMagnitude()

◆ clearDropTolerance()

◆ clearDualFeasibilityTolerance()

◆ clearDualizerThreshold()

◆ clearDualPricePrioritizeNorm()

◆ clearDualSmallPivotThreshold()

◆ clearDynamicallyAdjustRefactorizationPeriod()

◆ clearExploitSingletonColumnInInitialBasis()

◆ clearFeasibilityRule()

◆ clearHarrisToleranceRatio()

◆ clearInitialBasis()

◆ clearInitialConditionNumberThreshold()

◆ clearInitializeDevexWithColumnNorms()

◆ clearLogSearchProgress()

◆ clearLogToStdout()

◆ clearLuFactorizationPivotThreshold()

◆ clearMarkowitzSingularityThreshold()

◆ clearMarkowitzZlatevParameter()

◆ clearMaxDeterministicTime()

◆ clearMaxNumberOfIterations()

◆ clearMaxNumberOfReoptimizations()

◆ clearMaxTimeInSeconds()

◆ clearMaxValidMagnitude()

◆ clearMinimumAcceptablePivot()

◆ clearNumOmpThreads()

◆ clearObjectiveLowerLimit()

◆ clearObjectiveUpperLimit()

◆ clearOptimizationRule()

◆ clearPerturbCostsInDualSimplex()

◆ clearPreprocessorZeroTolerance()

◆ clearPrimalFeasibilityTolerance()

◆ clearProvideStrongOptimalGuarantee()

◆ clearPushToVertex()

◆ clearRandomSeed()

◆ clearRatioTestZeroThreshold()

◆ clearRecomputeEdgesNormThreshold()

◆ clearRecomputeReducedCostsThreshold()

◆ clearRefactorizationThreshold()

◆ clearRelativeCostPerturbation()

◆ clearRelativeMaxCostPerturbation()

◆ clearScalingMethod()

◆ clearSmallPivotThreshold()

◆ clearSolutionFeasibilityTolerance()

◆ clearSolveDualProblem()

◆ clearUseDedicatedDualFeasibilityAlgorithm()

◆ clearUseDualSimplex()

◆ clearUseImpliedFreePreprocessor()

◆ clearUseMiddleProductFormUpdate()

◆ clearUsePreprocessing()

◆ clearUseScaling()

◆ clearUseTransposedMatrix()

◆ getAllowSimplexAlgorithmChange()

◆ getBasisRefactorizationPeriod()

◆ getChangeStatusToImprecise()

◆ getCostScaling()

◆ getCrossoverBoundSnappingDistance()

◆ getDefaultInstanceForType()

◆ getDegenerateMinistepFactor()

◆ getDescriptor()

◆ getDescriptorForType()

◆ getDevexWeightsResetPeriod()

◆ getDropMagnitude()

◆ getDropTolerance()

◆ getDualFeasibilityTolerance()

◆ getDualizerThreshold()