com.google.ortools.glop.GlopParameters.Builder Class Reference

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	clone ()
Builder	setField (com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
Builder	clearField (com.google.protobuf.Descriptors.FieldDescriptor field)
Builder	clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)
Builder	setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value)
Builder	addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
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 ()
final Builder	setUnknownFields (final com.google.protobuf.UnknownFieldSet unknownFields)
final Builder	mergeUnknownFields (final com.google.protobuf.UnknownFieldSet unknownFields)

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

Protected Member Functions
com.google.protobuf.GeneratedMessageV3.FieldAccessorTable	internalGetFieldAccessorTable ()

Detailed Description

next id = 72

Protobuf type operations_research.glop.GlopParameters

Definition at line 3894 of file GlopParameters.java.

Member Function Documentation

addRepeatedField()

Builder com.google.ortools.glop.GlopParameters.Builder.addRepeatedField	(	com.google.protobuf.Descriptors.FieldDescriptor	field,
		java.lang.Object	value )

Definition at line 4287 of file GlopParameters.java.

build()

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

(

)

Definition at line 3999 of file GlopParameters.java.

buildPartial()

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

(

)

Definition at line 4008 of file GlopParameters.java.

clear()

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

(

)

Definition at line 3922 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 7099 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 6053 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 6523 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 5863 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 8531 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\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 7803 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 7155 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 8807 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 5761 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 5397 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 6255 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 8867 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 7475 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 6125 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 7411 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 4947 of file GlopParameters.java.

clearField()

Builder com.google.ortools.glop.GlopParameters.Builder.clearField

(

com.google.protobuf.Descriptors.FieldDescriptor

field

)

Definition at line 4271 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 5573 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 5925 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 8299 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 7351 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 8363 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 8419 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 6659 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 6975 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 6907 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 @ 3.50GHz).

TODO(user): Improve the correlation.

optional double max_deterministic_time = 45 [default = inf];

Returns

This builder for chaining.

Definition at line 6787 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 6847 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 6591 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 6715 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 8739 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 5697 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 7975 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 7639 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 7679 of file GlopParameters.java.

clearOneof()

Builder com.google.ortools.glop.GlopParameters.Builder.clearOneof

(

com.google.protobuf.Descriptors.OneofDescriptor

oneof

)

Definition at line 4276 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 5005 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 8043 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 7551 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 5305 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 6463 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 8607 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 7915 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 5477 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 5225 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 5153 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 5081 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 8195 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 8235 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 4889 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 5641 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 6339 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 6191 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 8127 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 7031 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 8663 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 7291 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 7211 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 5821 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 5989 of file GlopParameters.java.

clone()

Builder com.google.ortools.glop.GlopParameters.Builder.clone

(

)

Definition at line 4261 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 7066 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 6022 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 6494 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 5841 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 8476 of file GlopParameters.java.

getDefaultInstanceForType()

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

(

)

Definition at line 3994 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\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 7742 of file GlopParameters.java.

getDescriptor()

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

static

Definition at line 3899 of file GlopParameters.java.

getDescriptorForType()

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

(

)

Definition at line 3989 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 7128 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 8774 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 5730 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 5352 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 6224 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 8838 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 7444 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 6090 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 7382 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 4917 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 5526 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 5893 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 8268 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 7322 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 8332 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 8392 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 6626 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 6942 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 6878 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 @ 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 6752 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 6818 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 6558 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 6688 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 8702 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 5670 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 7946 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 7596 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 7660 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 4975 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 8010 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 7514 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 5266 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 6402 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 8570 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 7860 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 5438 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 5190 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 5118 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 5044 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 8162 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 8216 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 4867 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 5608 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 6298 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 6157 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 8086 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 7004 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 8636 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 7252 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 7184 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 5792 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 5958 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 7051 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 6008 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 6481 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 5833 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 8450 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\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 7713 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 7116 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 8759 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 5716 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 5331 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 6210 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 8825 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 7430 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 6074 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 7369 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 4905 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 5504 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 5880 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 8254 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 7309 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 8318 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 8380 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 6611 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 6927 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 6865 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 @ 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 6736 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 6805 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 6543 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 6676 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 8685 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 5658 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 7933 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 7576 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 7652 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 4963 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 7995 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 7497 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 5248 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 6373 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 8553 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 7834 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 5420 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 5174 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 5102 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 5027 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 8147 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 8208 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 4859 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 5593 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 6279 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 6143 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 8067 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 6992 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 8624 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 7234 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 7172 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 5779 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 5944 of file GlopParameters.java.

internalGetFieldAccessorTable()

com.google.protobuf.GeneratedMessageV3.FieldAccessorTable com.google.ortools.glop.GlopParameters.Builder.internalGetFieldAccessorTable ( )

protected

Definition at line 3905 of file GlopParameters.java.

isInitialized()

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

(

)

Definition at line 4484 of file GlopParameters.java.

mergeFrom() [1/3]

Builder com.google.ortools.glop.GlopParameters.Builder.mergeFrom

(

com.google.ortools.glop.GlopParameters

other

)

Definition at line 4302 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 4489 of file GlopParameters.java.

mergeFrom() [3/3]

Builder com.google.ortools.glop.GlopParameters.Builder.mergeFrom

(

com.google.protobuf.Message

other

)

Definition at line 4293 of file GlopParameters.java.

mergeUnknownFields()

final Builder com.google.ortools.glop.GlopParameters.Builder.mergeUnknownFields

(

final com.google.protobuf.UnknownFieldSet

unknownFields

)

Definition at line 8880 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 7081 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 6036 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 6507 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 5850 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 8502 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\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 7771 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 7140 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 8789 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 5744 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 5373 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 6238 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 8851 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 7458 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 6106 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 7395 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 4930 of file GlopParameters.java.

setField()

Builder com.google.ortools.glop.GlopParameters.Builder.setField	(	com.google.protobuf.Descriptors.FieldDescriptor	field,
		java.lang.Object	value )

Definition at line 4265 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 5548 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 5907 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 8282 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 7335 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 8346 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 8404 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 6641 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 6957 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 6891 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 @ 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 6768 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 6831 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 6573 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 6700 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 8719 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 5682 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 7959 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 7616 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 7668 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 4988 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 8025 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 7531 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 5284 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 6431 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 8587 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 7886 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 5456 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 5206 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 5134 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 5061 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 8177 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 8224 of file GlopParameters.java.

setRepeatedField()

Builder com.google.ortools.glop.GlopParameters.Builder.setRepeatedField	(	com.google.protobuf.Descriptors.FieldDescriptor	field,
		int	index,
		java.lang.Object	value )

Definition at line 4281 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 4876 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 5623 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 6317 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 6172 of file GlopParameters.java.

setUnknownFields()

final Builder com.google.ortools.glop.GlopParameters.Builder.setUnknownFields

(

final com.google.protobuf.UnknownFieldSet

unknownFields

)

Definition at line 8874 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 8105 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 7016 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 8648 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 7270 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 7196 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 5805 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 5972 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