Uses of Class
com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder
Packages that use PrimalDualHybridGradientParams.Builder
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Uses of PrimalDualHybridGradientParams.Builder in com.google.ortools.pdlp
Subclasses with type arguments of type PrimalDualHybridGradientParams.Builder in com.google.ortools.pdlpModifier and TypeClassDescriptionstatic final classParameters for PrimalDualHybridGradient() in primal_dual_hybrid_gradient.h.Methods in com.google.ortools.pdlp that return PrimalDualHybridGradientParams.BuilderModifier and TypeMethodDescriptionPrimalDualHybridGradientParams.Builder.addAllRandomProjectionSeeds(Iterable<? extends Integer> values) Seeds for generating (pseudo-)random projections of iterates during termination checks.PrimalDualHybridGradientParams.Builder.addRandomProjectionSeeds(int value) Seeds for generating (pseudo-)random projections of iterates during termination checks.PrimalDualHybridGradientParams.Builder.clear()PrimalDualHybridGradientParams.Builder.clearAdaptiveLinesearchParameters()optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;PrimalDualHybridGradientParams.Builder.clearApplyFeasibilityPolishingAfterLimitsReached()If true, feasibility polishing will be applied after the iteration limit, kkt limit, or time limit is reached.PrimalDualHybridGradientParams.Builder.clearApplyFeasibilityPolishingIfSolverIsInterrupted()If true, feasibility polishing will be applied after the solver is interrupted.PrimalDualHybridGradientParams.Builder.clearDiagonalQpTrustRegionSolverTolerance()The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter.PrimalDualHybridGradientParams.Builder.clearHandleSomePrimalGradientsOnFiniteBoundsAsResiduals()See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.PrimalDualHybridGradientParams.Builder.clearInfiniteConstraintBoundThreshold()Constraint bounds with absolute value at least this threshold are replaced with infinities.PrimalDualHybridGradientParams.Builder.clearInitialPrimalWeight()The initial value of the primal weight (i.e., the ratio of primal and dual step sizes).PrimalDualHybridGradientParams.Builder.clearInitialStepSizeScaling()Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).PrimalDualHybridGradientParams.Builder.clearL2NormRescaling()If true, applies L_2 norm rescaling after the Ruiz rescaling.PrimalDualHybridGradientParams.Builder.clearLinesearchRule()Linesearch rule applied at each major iteration.PrimalDualHybridGradientParams.Builder.clearLInfRuizIterations()Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix.PrimalDualHybridGradientParams.Builder.clearLogIntervalSeconds()Time between iteration-level statistics logging (if `verbosity_level > 1`).PrimalDualHybridGradientParams.Builder.clearMajorIterationFrequency()The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight.PrimalDualHybridGradientParams.Builder.clearMalitskyPockParameters()optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;PrimalDualHybridGradientParams.Builder.clearNecessaryReductionForRestart()For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function by this amount triggers a restart if, additionally, the quality of the iterates appears to be getting worse.PrimalDualHybridGradientParams.Builder.clearNumShards()For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards.PrimalDualHybridGradientParams.Builder.clearNumThreads()The number of threads to use.PrimalDualHybridGradientParams.Builder.clearPresolveOptions()optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;PrimalDualHybridGradientParams.Builder.clearPrimalWeightUpdateSmoothing()This parameter controls exponential smoothing of log(primal_weight) when a primal weight update occurs (i.e., when the ratio of primal and dual step sizes is adjusted).PrimalDualHybridGradientParams.Builder.clearRandomProjectionSeeds()Seeds for generating (pseudo-)random projections of iterates during termination checks.PrimalDualHybridGradientParams.Builder.clearRecordIterationStats()If true, the iteration_stats field of the SolveLog output will be populated at every iteration.PrimalDualHybridGradientParams.Builder.clearRestartStrategy()NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.PrimalDualHybridGradientParams.Builder.clearSchedulerType()The type of scheduler used for CPU multi-threading.PrimalDualHybridGradientParams.Builder.clearSufficientReductionForRestart()For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart.PrimalDualHybridGradientParams.Builder.clearTerminationCheckFrequency()The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats.PrimalDualHybridGradientParams.Builder.clearTerminationCriteria()optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;PrimalDualHybridGradientParams.Builder.clearUseDiagonalQpTrustRegionSolver()When solving QPs with diagonal objective matrices, this option can be turned on to enable an experimental solver that avoids linearization of the quadratic term.PrimalDualHybridGradientParams.Builder.clearUseFeasibilityPolishing()If true, periodically runs feasibility polishing, which attempts to move from latest average iterate to one that is closer to feasibility (i.e., has smaller primal and dual residuals) while probably increasing the objective gap.PrimalDualHybridGradientParams.Builder.clearVerbosityLevel()The verbosity of logging. 0: No informational logging.FeasibilityPolishingDetails.Builder.getParamsBuilder()optional .operations_research.pdlp.PrimalDualHybridGradientParams params = 3;SolveLog.Builder.getParamsBuilder()If solved with PDLP, the parameters for this solve.PrimalDualHybridGradientParams.Builder.mergeAdaptiveLinesearchParameters(AdaptiveLinesearchParams value) optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;PrimalDualHybridGradientParams.Builder.mergeFrom(PrimalDualHybridGradientParams other) PrimalDualHybridGradientParams.Builder.mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) PrimalDualHybridGradientParams.Builder.mergeFrom(com.google.protobuf.Message other) PrimalDualHybridGradientParams.Builder.mergeMalitskyPockParameters(MalitskyPockParams value) optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;PrimalDualHybridGradientParams.Builder.mergePresolveOptions(PrimalDualHybridGradientParams.PresolveOptions value) optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;PrimalDualHybridGradientParams.Builder.mergeTerminationCriteria(TerminationCriteria value) optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;PrimalDualHybridGradientParams.newBuilder()PrimalDualHybridGradientParams.newBuilder(PrimalDualHybridGradientParams prototype) PrimalDualHybridGradientParams.newBuilderForType()protected PrimalDualHybridGradientParams.BuilderPrimalDualHybridGradientParams.newBuilderForType(com.google.protobuf.AbstractMessage.BuilderParent parent) PrimalDualHybridGradientParams.Builder.setAdaptiveLinesearchParameters(AdaptiveLinesearchParams value) optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;PrimalDualHybridGradientParams.Builder.setAdaptiveLinesearchParameters(AdaptiveLinesearchParams.Builder builderForValue) optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;PrimalDualHybridGradientParams.Builder.setApplyFeasibilityPolishingAfterLimitsReached(boolean value) If true, feasibility polishing will be applied after the iteration limit, kkt limit, or time limit is reached.PrimalDualHybridGradientParams.Builder.setApplyFeasibilityPolishingIfSolverIsInterrupted(boolean value) If true, feasibility polishing will be applied after the solver is interrupted.PrimalDualHybridGradientParams.Builder.setDiagonalQpTrustRegionSolverTolerance(double value) The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter.PrimalDualHybridGradientParams.Builder.setHandleSomePrimalGradientsOnFiniteBoundsAsResiduals(boolean value) See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.PrimalDualHybridGradientParams.Builder.setInfiniteConstraintBoundThreshold(double value) Constraint bounds with absolute value at least this threshold are replaced with infinities.PrimalDualHybridGradientParams.Builder.setInitialPrimalWeight(double value) The initial value of the primal weight (i.e., the ratio of primal and dual step sizes).PrimalDualHybridGradientParams.Builder.setInitialStepSizeScaling(double value) Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).PrimalDualHybridGradientParams.Builder.setL2NormRescaling(boolean value) If true, applies L_2 norm rescaling after the Ruiz rescaling.PrimalDualHybridGradientParams.Builder.setLinesearchRule(PrimalDualHybridGradientParams.LinesearchRule value) Linesearch rule applied at each major iteration.PrimalDualHybridGradientParams.Builder.setLInfRuizIterations(int value) Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix.PrimalDualHybridGradientParams.Builder.setLogIntervalSeconds(double value) Time between iteration-level statistics logging (if `verbosity_level > 1`).PrimalDualHybridGradientParams.Builder.setMajorIterationFrequency(int value) The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight.PrimalDualHybridGradientParams.Builder.setMalitskyPockParameters(MalitskyPockParams value) optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;PrimalDualHybridGradientParams.Builder.setMalitskyPockParameters(MalitskyPockParams.Builder builderForValue) optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;PrimalDualHybridGradientParams.Builder.setNecessaryReductionForRestart(double value) For ADAPTIVE_HEURISTIC only: A relative reduction in the potential function by this amount triggers a restart if, additionally, the quality of the iterates appears to be getting worse.PrimalDualHybridGradientParams.Builder.setNumShards(int value) For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards.PrimalDualHybridGradientParams.Builder.setNumThreads(int value) The number of threads to use.PrimalDualHybridGradientParams.Builder.setPresolveOptions(PrimalDualHybridGradientParams.PresolveOptions value) optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;PrimalDualHybridGradientParams.Builder.setPresolveOptions(PrimalDualHybridGradientParams.PresolveOptions.Builder builderForValue) optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;PrimalDualHybridGradientParams.Builder.setPrimalWeightUpdateSmoothing(double value) This parameter controls exponential smoothing of log(primal_weight) when a primal weight update occurs (i.e., when the ratio of primal and dual step sizes is adjusted).PrimalDualHybridGradientParams.Builder.setRandomProjectionSeeds(int index, int value) Seeds for generating (pseudo-)random projections of iterates during termination checks.PrimalDualHybridGradientParams.Builder.setRecordIterationStats(boolean value) If true, the iteration_stats field of the SolveLog output will be populated at every iteration.PrimalDualHybridGradientParams.Builder.setRestartStrategy(PrimalDualHybridGradientParams.RestartStrategy value) NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default.PrimalDualHybridGradientParams.Builder.setSchedulerType(SchedulerType value) The type of scheduler used for CPU multi-threading.PrimalDualHybridGradientParams.Builder.setSufficientReductionForRestart(double value) For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart.PrimalDualHybridGradientParams.Builder.setTerminationCheckFrequency(int value) The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats.PrimalDualHybridGradientParams.Builder.setTerminationCriteria(TerminationCriteria value) optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;PrimalDualHybridGradientParams.Builder.setTerminationCriteria(TerminationCriteria.Builder builderForValue) optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;PrimalDualHybridGradientParams.Builder.setUseDiagonalQpTrustRegionSolver(boolean value) When solving QPs with diagonal objective matrices, this option can be turned on to enable an experimental solver that avoids linearization of the quadratic term.PrimalDualHybridGradientParams.Builder.setUseFeasibilityPolishing(boolean value) If true, periodically runs feasibility polishing, which attempts to move from latest average iterate to one that is closer to feasibility (i.e., has smaller primal and dual residuals) while probably increasing the objective gap.PrimalDualHybridGradientParams.Builder.setVerbosityLevel(int value) The verbosity of logging. 0: No informational logging.PrimalDualHybridGradientParams.toBuilder()Methods in com.google.ortools.pdlp with parameters of type PrimalDualHybridGradientParams.BuilderModifier and TypeMethodDescriptionFeasibilityPolishingDetails.Builder.setParams(PrimalDualHybridGradientParams.Builder builderForValue) optional .operations_research.pdlp.PrimalDualHybridGradientParams params = 3;SolveLog.Builder.setParams(PrimalDualHybridGradientParams.Builder builderForValue) If solved with PDLP, the parameters for this solve.