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Google OR-Tools v9.11
a fast and portable software suite for combinatorial optimization
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Google OR-Tools v9.11
a fast and portable software suite for combinatorial optimization
|
Static Public Member Functions | |
| static final com.google.protobuf.Descriptors.Descriptor | getDescriptor () |
Protected Member Functions | |
| com.google.protobuf.GeneratedMessage.FieldAccessorTable | internalGetFieldAccessorTable () |
Parameters for PrimalDualHybridGradient() in primal_dual_hybrid_gradient.h. While the defaults are generally good, it is usually worthwhile to perform a parameter sweep to find good settings for a particular family of problems. The following parameters should be considered for tuning: - restart_strategy (jointly with major_iteration_frequency) - primal_weight_update_smoothing (jointly with initial_primal_weight) - presolve_options.use_glop - l_inf_ruiz_iterations - l2_norm_rescaling In addition, tune num_threads to speed up the solve.
Protobuf type operations_research.pdlp.PrimalDualHybridGradientParams
Definition at line 2790 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.addAllRandomProjectionSeeds | ( | java.lang.Iterable<? extends java.lang.Integer > | values | ) |
Seeds for generating (pseudo-)random projections of iterates during termination checks. For each seed, the projection of the primal and dual solutions onto random planes in primal and dual space will be computed and added the IterationStats if record_iteration_stats is true. The random planes generated will be determined by the seeds, the primal and dual dimensions, and num_threads.
repeated int32 random_projection_seeds = 28 [packed = true];
| values | The randomProjectionSeeds to add. |
Definition at line 5067 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.addRandomProjectionSeeds | ( | int | value | ) |
Seeds for generating (pseudo-)random projections of iterates during termination checks. For each seed, the projection of the primal and dual solutions onto random planes in primal and dual space will be computed and added the IterationStats if record_iteration_stats is true. The random planes generated will be determined by the seeds, the primal and dual dimensions, and num_threads.
repeated int32 random_projection_seeds = 28 [packed = true];
| value | The randomProjectionSeeds to add. |
Definition at line 5045 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.build | ( | ) |
Definition at line 2887 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.buildPartial | ( | ) |
Definition at line 2896 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clear | ( | ) |
Definition at line 2827 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearAdaptiveLinesearchParameters | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Definition at line 4722 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. Smaller values imply smaller relative error of the final solution vector. TODO(user): Find an expression for the final relative error.
optional double diagonal_qp_trust_region_solver_tolerance = 24 [default = 1e-08];
Definition at line 5377 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearHandleSomePrimalGradientsOnFiniteBoundsAsResiduals | ( | ) |
See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.
optional bool handle_some_primal_gradients_on_finite_bounds_as_residuals = 29 [default = true];
Definition at line 5229 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearInfiniteConstraintBoundThreshold | ( | ) |
Constraint bounds with absolute value at least this threshold are replaced with infinities. NOTE: This primarily affects the relative convergence criteria. A smaller value makes the relative convergence criteria stronger. It also affects the problem statistics LOG()ed at the start of the run, and the default initial primal weight, since that is based on the norm of the bounds.
optional double infinite_constraint_bound_threshold = 22 [default = inf];
Definition at line 5165 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearInitialPrimalWeight | ( | ) |
Public Member Functions inherited from
The initial value of the primal weight (i.e., the ratio of primal and dual
step sizes). The primal weight remains fixed throughout the solve if
primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
the norm of the objective vector to the L2 norm of the combined constraint
bounds vector (as defined above). If this ratio is not finite and positive,
then the default is 1.0 instead. For tuning, try powers of 10, for example,
from 10^{-6} to 10^6.
optional double initial_primal_weight = 8;
Definition at line 4201 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearInitialStepSizeScaling | ( | ) |
Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
optional double initial_step_size_scaling = 25 [default = 1];
Definition at line 4942 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearL2NormRescaling | ( | ) |
If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically this has been found to help convergence.
optional bool l2_norm_rescaling = 10 [default = true];
Definition at line 4446 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearLinesearchRule | ( | ) |
Linesearch rule applied at each major iteration.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Definition at line 4640 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearLInfRuizIterations | ( | ) |
Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix. Zero disables this rescaling pass. Recommended values to try when tuning are 0, 5, and 10.
optional int32 l_inf_ruiz_iterations = 9 [default = 5];
Definition at line 4386 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearLogIntervalSeconds | ( | ) |
Time between iteration-level statistics logging (if `verbosity_level > 1`). Since iteration-level statistics are only generated when performing termination checks, logs will be generated from next termination check after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the default) means log statistics at every termination check.
optional double log_interval_seconds = 31 [default = 0];
Definition at line 3831 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. Major iterations also trigger a termination check. For best performance using the NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale grid search over this parameter, for example, over powers of two. ADAPTIVE_HEURISTIC is mostly insensitive to this value.
optional int32 major_iteration_frequency = 4 [default = 64];
Definition at line 3907 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearMalitskyPockParameters | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Definition at line 4843 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. The value must be in the interval [sufficient_reduction_for_restart, 1). Smaller values make restarts less frequent, and larger values make them more frequent.
optional double necessary_reduction_for_restart = 17 [default = 0.9];
Definition at line 4582 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearNumShards | ( | ) |
For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards. Results are computed independently for each shard and then combined. As a consequence, the order of computation, and hence floating point roundoff, depends on the number of shards so reproducible results require using the same value for num_shards. However, for efficiency num_shards should a be at least num_threads, and preferably at least 4*num_threads to allow better load balancing. If num_shards is positive, the computation will use that many shards. Otherwise a default that depends on num_threads will be used.
optional int32 num_shards = 27 [default = 0];
Definition at line 3595 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearNumThreads | ( | ) |
The number of threads to use. Must be positive. Try various values of num_threads, up to the number of physical cores. Performance may not be monotonically increasing with the number of threads because of memory bandwidth limitations.
optional int32 num_threads = 2 [default = 1];
Definition at line 3507 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearPresolveOptions | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Definition at line 4283 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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). At 0.0, the primal weight will be frozen at its initial value and there will be no dynamic updates in the algorithm. At 1.0, there is no smoothing in the updates. The default of 0.5 generally performs well, but has been observed on occasion to trigger unstable swings in the primal weight. We recommend also trying 0.0 (disabling primal weight updates), in which case you must also tune initial_primal_weight.
optional double primal_weight_update_smoothing = 7 [default = 0.5];
Definition at line 4121 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearRandomProjectionSeeds | ( | ) |
Seeds for generating (pseudo-)random projections of iterates during termination checks. For each seed, the projection of the primal and dual solutions onto random planes in primal and dual space will be computed and added the IterationStats if record_iteration_stats is true. The random planes generated will be determined by the seeds, the primal and dual dimensions, and num_threads.
repeated int32 random_projection_seeds = 28 [packed = true];
Definition at line 5089 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearRecordIterationStats | ( | ) |
If true, the iteration_stats field of the SolveLog output will be populated at every iteration. Note that we only compute solution statistics at termination checks. Setting this parameter to true may substantially increase the size of the output.
optional bool record_iteration_stats = 3;
Definition at line 3663 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearRestartStrategy | ( | ) |
NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default. If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune major_iteration_frequency.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];
Definition at line 4037 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. Must be between 0.0 and 1.0.
optional double sufficient_reduction_for_restart = 11 [default = 0.1];
Definition at line 4510 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearTerminationCheckFrequency | ( | ) |
The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats. Termination checks do not affect algorithmic progress unless termination is triggered.
optional int32 termination_check_frequency = 5 [default = 64];
Definition at line 3971 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearTerminationCriteria | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Definition at line 3400 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. The `diagonal_qp_solver_accuracy` parameter controls the solve accuracy. TODO(user): Turn this option on by default for quadratic programs after numerical evaluation.
optional bool use_diagonal_qp_trust_region_solver = 23 [default = false];
Definition at line 5305 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. This is useful primarily when the feasibility tolerances are fairly tight and the objective gap tolerance is somewhat looser. Note that this does not change the termination criteria, but rather can help achieve the termination criteria more quickly when the objective gap is not as important as feasibility. `use_feasibility_polishing` cannot be used with glop presolve, and requires `handle_some_primal_gradients_on_finite_bounds_as_residuals == false`. `use_feasibility_polishing` can only be used with linear programs. Feasibility polishing runs two separate phases, primal feasibility and dual feasibility. The primal feasibility phase runs PDHG on the primal feasibility problem (obtained by changing the objective vector to all zeros), using the average primal iterate and zero dual (which is optimal for the primal feasibility problem) as the initial solution. The dual feasibility phase runs PDHG on the dual feasibility problem (obtained by changing all finite variable and constraint bounds to zero), using the average dual iterate and zero primal (which is optimal for the dual feasibility problem) as the initial solution. The primal solution from the primal feasibility phase and dual solution from the dual feasibility phase are then combined (forming a solution of type `POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION`) and checked against the termination criteria.
optional bool use_feasibility_polishing = 30 [default = false];
Definition at line 5533 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.clearVerbosityLevel | ( | ) |
The verbosity of logging. 0: No informational logging. (Errors are logged.) 1: Summary statistics only. No iteration-level details. 2: A table of iteration-level statistics is logged. (See ToShortString() in primal_dual_hybrid_gradient.cc). 3: A more detailed table of iteration-level statistics is logged. (See ToString() in primal_dual_hybrid_gradient.cc). 4: For iteration-level details, prints the statistics of both the average (prefixed with A) and the current iterate (prefixed with C). Also prints internal algorithmic state and details. Logging at levels 2-4 also includes messages from level 1.
optional int32 verbosity_level = 26 [default = 0];
Definition at line 3759 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.AdaptiveLinesearchParams com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getAdaptiveLinesearchParameters | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4661 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.AdaptiveLinesearchParams.Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getAdaptiveLinesearchParametersBuilder | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Definition at line 4735 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.AdaptiveLinesearchParamsOrBuilder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getAdaptiveLinesearchParametersOrBuilder | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4743 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getDefaultInstanceForType | ( | ) |
Definition at line 2882 of file PrimalDualHybridGradientParams.java.
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static |
Definition at line 2795 of file PrimalDualHybridGradientParams.java.
| com.google.protobuf.Descriptors.Descriptor com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getDescriptorForType | ( | ) |
Definition at line 2877 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getDiagonalQpTrustRegionSolverTolerance | ( | ) |
The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter. Smaller values imply smaller relative error of the final solution vector. TODO(user): Find an expression for the final relative error.
optional double diagonal_qp_trust_region_solver_tolerance = 24 [default = 1e-08];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5342 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getHandleSomePrimalGradientsOnFiniteBoundsAsResiduals | ( | ) |
See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.
optional bool handle_some_primal_gradients_on_finite_bounds_as_residuals = 29 [default = true];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5198 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getInfiniteConstraintBoundThreshold | ( | ) |
Constraint bounds with absolute value at least this threshold are replaced with infinities. NOTE: This primarily affects the relative convergence criteria. A smaller value makes the relative convergence criteria stronger. It also affects the problem statistics LOG()ed at the start of the run, and the default initial primal weight, since that is based on the norm of the bounds.
optional double infinite_constraint_bound_threshold = 22 [default = inf];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5128 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getInitialPrimalWeight | ( | ) |
The initial value of the primal weight (i.e., the ratio of primal and dual
step sizes). The primal weight remains fixed throughout the solve if
primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
the norm of the objective vector to the L2 norm of the combined constraint
bounds vector (as defined above). If this ratio is not finite and positive,
then the default is 1.0 instead. For tuning, try powers of 10, for example,
from 10^{-6} to 10^6.
optional double initial_primal_weight = 8;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4162 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getInitialStepSizeScaling | ( | ) |
Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
optional double initial_step_size_scaling = 25 [default = 1];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4913 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getL2NormRescaling | ( | ) |
If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically this has been found to help convergence.
optional bool l2_norm_rescaling = 10 [default = true];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4417 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams.LinesearchRule com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getLinesearchRule | ( | ) |
Linesearch rule applied at each major iteration.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4610 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getLInfRuizIterations | ( | ) |
Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix. Zero disables this rescaling pass. Recommended values to try when tuning are 0, 5, and 10.
optional int32 l_inf_ruiz_iterations = 9 [default = 5];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4355 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getLogIntervalSeconds | ( | ) |
Time between iteration-level statistics logging (if `verbosity_level > 1`). Since iteration-level statistics are only generated when performing termination checks, logs will be generated from next termination check after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the default) means log statistics at every termination check.
optional double log_interval_seconds = 31 [default = 0];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3796 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getMajorIterationFrequency | ( | ) |
The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight. Major iterations also trigger a termination check. For best performance using the NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale grid search over this parameter, for example, over powers of two. ADAPTIVE_HEURISTIC is mostly insensitive to this value.
optional int32 major_iteration_frequency = 4 [default = 64];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3870 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.MalitskyPockParams com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getMalitskyPockParameters | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4782 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.MalitskyPockParams.Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getMalitskyPockParametersBuilder | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Definition at line 4856 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.MalitskyPockParamsOrBuilder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getMalitskyPockParametersOrBuilder | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4864 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getNecessaryReductionForRestart | ( | ) |
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. The value must be in the interval [sufficient_reduction_for_restart, 1). Smaller values make restarts less frequent, and larger values make them more frequent.
optional double necessary_reduction_for_restart = 17 [default = 0.9];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4547 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getNumShards | ( | ) |
For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards. Results are computed independently for each shard and then combined. As a consequence, the order of computation, and hence floating point roundoff, depends on the number of shards so reproducible results require using the same value for num_shards. However, for efficiency num_shards should a be at least num_threads, and preferably at least 4*num_threads to allow better load balancing. If num_shards is positive, the computation will use that many shards. Otherwise a default that depends on num_threads will be used.
optional int32 num_shards = 27 [default = 0];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3552 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getNumThreads | ( | ) |
The number of threads to use. Must be positive. Try various values of num_threads, up to the number of physical cores. Performance may not be monotonically increasing with the number of threads because of memory bandwidth limitations.
optional int32 num_threads = 2 [default = 1];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3474 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams.PresolveOptions com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getPresolveOptions | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4222 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams.PresolveOptions.Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getPresolveOptionsBuilder | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Definition at line 4296 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams.PresolveOptionsOrBuilder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getPresolveOptionsOrBuilder | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4304 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getPrimalWeightUpdateSmoothing | ( | ) |
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). At 0.0, the primal weight will be frozen at its initial value and there will be no dynamic updates in the algorithm. At 1.0, there is no smoothing in the updates. The default of 0.5 generally performs well, but has been observed on occasion to trigger unstable swings in the primal weight. We recommend also trying 0.0 (disabling primal weight updates), in which case you must also tune initial_primal_weight.
optional double primal_weight_update_smoothing = 7 [default = 0.5];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4080 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getRandomProjectionSeeds | ( | int | index | ) |
Seeds for generating (pseudo-)random projections of iterates during termination checks. For each seed, the projection of the primal and dual solutions onto random planes in primal and dual space will be computed and added the IterationStats if record_iteration_stats is true. The random planes generated will be determined by the seeds, the primal and dual dimensions, and num_threads.
repeated int32 random_projection_seeds = 28 [packed = true];
| index | The index of the element to return. |
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5004 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getRandomProjectionSeedsCount | ( | ) |
Seeds for generating (pseudo-)random projections of iterates during termination checks. For each seed, the projection of the primal and dual solutions onto random planes in primal and dual space will be computed and added the IterationStats if record_iteration_stats is true. The random planes generated will be determined by the seeds, the primal and dual dimensions, and num_threads.
repeated int32 random_projection_seeds = 28 [packed = true];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4987 of file PrimalDualHybridGradientParams.java.
| java.util.List< java.lang.Integer > com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getRandomProjectionSeedsList | ( | ) |
Seeds for generating (pseudo-)random projections of iterates during termination checks. For each seed, the projection of the primal and dual solutions onto random planes in primal and dual space will be computed and added the IterationStats if record_iteration_stats is true. The random planes generated will be determined by the seeds, the primal and dual dimensions, and num_threads.
repeated int32 random_projection_seeds = 28 [packed = true];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4970 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getRecordIterationStats | ( | ) |
If true, the iteration_stats field of the SolveLog output will be populated at every iteration. Note that we only compute solution statistics at termination checks. Setting this parameter to true may substantially increase the size of the output.
optional bool record_iteration_stats = 3;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3630 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.PrimalDualHybridGradientParams.RestartStrategy com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getRestartStrategy | ( | ) |
NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default. If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune major_iteration_frequency.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4003 of file PrimalDualHybridGradientParams.java.
| double com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getSufficientReductionForRestart | ( | ) |
For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart. Must be between 0.0 and 1.0.
optional double sufficient_reduction_for_restart = 11 [default = 0.1];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4479 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getTerminationCheckFrequency | ( | ) |
The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats. Termination checks do not affect algorithmic progress unless termination is triggered.
optional int32 termination_check_frequency = 5 [default = 64];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3940 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.TerminationCriteria com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getTerminationCriteria | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3339 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.TerminationCriteria.Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getTerminationCriteriaBuilder | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Definition at line 3413 of file PrimalDualHybridGradientParams.java.
| com.google.ortools.pdlp.TerminationCriteriaOrBuilder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getTerminationCriteriaOrBuilder | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3421 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getUseDiagonalQpTrustRegionSolver | ( | ) |
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. The `diagonal_qp_solver_accuracy` parameter controls the solve accuracy. TODO(user): Turn this option on by default for quadratic programs after numerical evaluation.
optional bool use_diagonal_qp_trust_region_solver = 23 [default = false];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5268 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getUseFeasibilityPolishing | ( | ) |
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. This is useful primarily when the feasibility tolerances are fairly tight and the objective gap tolerance is somewhat looser. Note that this does not change the termination criteria, but rather can help achieve the termination criteria more quickly when the objective gap is not as important as feasibility. `use_feasibility_polishing` cannot be used with glop presolve, and requires `handle_some_primal_gradients_on_finite_bounds_as_residuals == false`. `use_feasibility_polishing` can only be used with linear programs. Feasibility polishing runs two separate phases, primal feasibility and dual feasibility. The primal feasibility phase runs PDHG on the primal feasibility problem (obtained by changing the objective vector to all zeros), using the average primal iterate and zero dual (which is optimal for the primal feasibility problem) as the initial solution. The dual feasibility phase runs PDHG on the dual feasibility problem (obtained by changing all finite variable and constraint bounds to zero), using the average dual iterate and zero primal (which is optimal for the dual feasibility problem) as the initial solution. The primal solution from the primal feasibility phase and dual solution from the dual feasibility phase are then combined (forming a solution of type `POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION`) and checked against the termination criteria.
optional bool use_feasibility_polishing = 30 [default = false];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5456 of file PrimalDualHybridGradientParams.java.
| int com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.getVerbosityLevel | ( | ) |
The verbosity of logging. 0: No informational logging. (Errors are logged.) 1: Summary statistics only. No iteration-level details. 2: A table of iteration-level statistics is logged. (See ToShortString() in primal_dual_hybrid_gradient.cc). 3: A more detailed table of iteration-level statistics is logged. (See ToString() in primal_dual_hybrid_gradient.cc). 4: For iteration-level details, prints the statistics of both the average (prefixed with A) and the current iterate (prefixed with C). Also prints internal algorithmic state and details. Logging at levels 2-4 also includes messages from level 1.
optional int32 verbosity_level = 26 [default = 0];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3712 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasAdaptiveLinesearchParameters | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4654 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasDiagonalQpTrustRegionSolverTolerance | ( | ) |
The solve tolerance of the experimental trust region solver for diagonal QPs, controlling the accuracy of binary search over a one-dimensional scaling parameter. Smaller values imply smaller relative error of the final solution vector. TODO(user): Find an expression for the final relative error.
optional double diagonal_qp_trust_region_solver_tolerance = 24 [default = 1e-08];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5326 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasHandleSomePrimalGradientsOnFiniteBoundsAsResiduals | ( | ) |
See https://developers.google.com/optimization/lp/pdlp_math#treating_some_variable_bounds_as_infinite for a description of this flag.
optional bool handle_some_primal_gradients_on_finite_bounds_as_residuals = 29 [default = true];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5184 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasInfiniteConstraintBoundThreshold | ( | ) |
Constraint bounds with absolute value at least this threshold are replaced with infinities. NOTE: This primarily affects the relative convergence criteria. A smaller value makes the relative convergence criteria stronger. It also affects the problem statistics LOG()ed at the start of the run, and the default initial primal weight, since that is based on the norm of the bounds.
optional double infinite_constraint_bound_threshold = 22 [default = inf];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5111 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasInitialPrimalWeight | ( | ) |
The initial value of the primal weight (i.e., the ratio of primal and dual
step sizes). The primal weight remains fixed throughout the solve if
primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
the norm of the objective vector to the L2 norm of the combined constraint
bounds vector (as defined above). If this ratio is not finite and positive,
then the default is 1.0 instead. For tuning, try powers of 10, for example,
from 10^{-6} to 10^6.
optional double initial_primal_weight = 8;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4144 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasInitialStepSizeScaling | ( | ) |
Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
optional double initial_step_size_scaling = 25 [default = 1];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4900 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasL2NormRescaling | ( | ) |
If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically this has been found to help convergence.
optional bool l2_norm_rescaling = 10 [default = true];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4404 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasLinesearchRule | ( | ) |
Linesearch rule applied at each major iteration.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4598 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasLInfRuizIterations | ( | ) |
Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix. Zero disables this rescaling pass. Recommended values to try when tuning are 0, 5, and 10.
optional int32 l_inf_ruiz_iterations = 9 [default = 5];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4341 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasLogIntervalSeconds | ( | ) |
Time between iteration-level statistics logging (if `verbosity_level > 1`). Since iteration-level statistics are only generated when performing termination checks, logs will be generated from next termination check after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the default) means log statistics at every termination check.
optional double log_interval_seconds = 31 [default = 0];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3780 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasMajorIterationFrequency | ( | ) |
The frequency at which extra work is performed to make major algorithmic decisions, e.g., performing restarts and updating the primal weight. Major iterations also trigger a termination check. For best performance using the NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale grid search over this parameter, for example, over powers of two. ADAPTIVE_HEURISTIC is mostly insensitive to this value.
optional int32 major_iteration_frequency = 4 [default = 64];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3853 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasMalitskyPockParameters | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4775 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasNecessaryReductionForRestart | ( | ) |
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. The value must be in the interval [sufficient_reduction_for_restart, 1). Smaller values make restarts less frequent, and larger values make them more frequent.
optional double necessary_reduction_for_restart = 17 [default = 0.9];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4531 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasNumShards | ( | ) |
For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards. Results are computed independently for each shard and then combined. As a consequence, the order of computation, and hence floating point roundoff, depends on the number of shards so reproducible results require using the same value for num_shards. However, for efficiency num_shards should a be at least num_threads, and preferably at least 4*num_threads to allow better load balancing. If num_shards is positive, the computation will use that many shards. Otherwise a default that depends on num_threads will be used.
optional int32 num_shards = 27 [default = 0];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3532 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasNumThreads | ( | ) |
The number of threads to use. Must be positive. Try various values of num_threads, up to the number of physical cores. Performance may not be monotonically increasing with the number of threads because of memory bandwidth limitations.
optional int32 num_threads = 2 [default = 1];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3459 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasPresolveOptions | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4215 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasPrimalWeightUpdateSmoothing | ( | ) |
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). At 0.0, the primal weight will be frozen at its initial value and there will be no dynamic updates in the algorithm. At 1.0, there is no smoothing in the updates. The default of 0.5 generally performs well, but has been observed on occasion to trigger unstable swings in the primal weight. We recommend also trying 0.0 (disabling primal weight updates), in which case you must also tune initial_primal_weight.
optional double primal_weight_update_smoothing = 7 [default = 0.5];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4061 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasRecordIterationStats | ( | ) |
If true, the iteration_stats field of the SolveLog output will be populated at every iteration. Note that we only compute solution statistics at termination checks. Setting this parameter to true may substantially increase the size of the output.
optional bool record_iteration_stats = 3;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3615 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasRestartStrategy | ( | ) |
NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default. If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune major_iteration_frequency.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3989 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasSufficientReductionForRestart | ( | ) |
For ADAPTIVE_HEURISTIC and ADAPTIVE_DISTANCE_BASED only: A relative reduction in the potential function by this amount always triggers a restart. Must be between 0.0 and 1.0.
optional double sufficient_reduction_for_restart = 11 [default = 0.1];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 4465 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasTerminationCheckFrequency | ( | ) |
The frequency (based on a counter reset every major iteration) to check for termination (involves extra work) and log iteration stats. Termination checks do not affect algorithmic progress unless termination is triggered.
optional int32 termination_check_frequency = 5 [default = 64];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3926 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasTerminationCriteria | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3332 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasUseDiagonalQpTrustRegionSolver | ( | ) |
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. The `diagonal_qp_solver_accuracy` parameter controls the solve accuracy. TODO(user): Turn this option on by default for quadratic programs after numerical evaluation.
optional bool use_diagonal_qp_trust_region_solver = 23 [default = false];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5251 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasUseFeasibilityPolishing | ( | ) |
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. This is useful primarily when the feasibility tolerances are fairly tight and the objective gap tolerance is somewhat looser. Note that this does not change the termination criteria, but rather can help achieve the termination criteria more quickly when the objective gap is not as important as feasibility. `use_feasibility_polishing` cannot be used with glop presolve, and requires `handle_some_primal_gradients_on_finite_bounds_as_residuals == false`. `use_feasibility_polishing` can only be used with linear programs. Feasibility polishing runs two separate phases, primal feasibility and dual feasibility. The primal feasibility phase runs PDHG on the primal feasibility problem (obtained by changing the objective vector to all zeros), using the average primal iterate and zero dual (which is optimal for the primal feasibility problem) as the initial solution. The dual feasibility phase runs PDHG on the dual feasibility problem (obtained by changing all finite variable and constraint bounds to zero), using the average dual iterate and zero primal (which is optimal for the dual feasibility problem) as the initial solution. The primal solution from the primal feasibility phase and dual solution from the dual feasibility phase are then combined (forming a solution of type `POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION`) and checked against the termination criteria.
optional bool use_feasibility_polishing = 30 [default = false];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 5419 of file PrimalDualHybridGradientParams.java.
| boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.hasVerbosityLevel | ( | ) |
The verbosity of logging. 0: No informational logging. (Errors are logged.) 1: Summary statistics only. No iteration-level details. 2: A table of iteration-level statistics is logged. (See ToShortString() in primal_dual_hybrid_gradient.cc). 3: A more detailed table of iteration-level statistics is logged. (See ToString() in primal_dual_hybrid_gradient.cc). 4: For iteration-level details, prints the statistics of both the average (prefixed with A) and the current iterate (prefixed with C). Also prints internal algorithmic state and details. Logging at levels 2-4 also includes messages from level 1.
optional int32 verbosity_level = 26 [default = 0];
Implements com.google.ortools.pdlp.PrimalDualHybridGradientParamsOrBuilder.
Definition at line 3690 of file PrimalDualHybridGradientParams.java.
|
protected |
Definition at line 2801 of file PrimalDualHybridGradientParams.java.
| final boolean com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.isInitialized | ( | ) |
Definition at line 3125 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.mergeAdaptiveLinesearchParameters | ( | com.google.ortools.pdlp.AdaptiveLinesearchParams | value | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Definition at line 4701 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.mergeFrom | ( | com.google.ortools.pdlp.PrimalDualHybridGradientParams | other | ) |
Definition at line 3031 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.mergeFrom | ( | com.google.protobuf.CodedInputStream | input, |
| com.google.protobuf.ExtensionRegistryLite | extensionRegistry ) throws java.io.IOException |
Definition at line 3130 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.mergeFrom | ( | com.google.protobuf.Message | other | ) |
Definition at line 3022 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.mergeMalitskyPockParameters | ( | com.google.ortools.pdlp.MalitskyPockParams | value | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Definition at line 4822 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.mergePresolveOptions | ( | com.google.ortools.pdlp.PrimalDualHybridGradientParams.PresolveOptions | value | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Definition at line 4262 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.mergeTerminationCriteria | ( | com.google.ortools.pdlp.TerminationCriteria | value | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Definition at line 3379 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setAdaptiveLinesearchParameters | ( | com.google.ortools.pdlp.AdaptiveLinesearchParams | value | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Definition at line 4671 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setAdaptiveLinesearchParameters | ( | com.google.ortools.pdlp.AdaptiveLinesearchParams.Builder | builderForValue | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Definition at line 4687 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. Smaller values imply smaller relative error of the final solution vector. TODO(user): Find an expression for the final relative error.
optional double diagonal_qp_trust_region_solver_tolerance = 24 [default = 1e-08];
| value | The diagonalQpTrustRegionSolverTolerance to set. |
Definition at line 5358 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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.
optional bool handle_some_primal_gradients_on_finite_bounds_as_residuals = 29 [default = true];
| value | The handleSomePrimalGradientsOnFiniteBoundsAsResiduals to set. |
Definition at line 5212 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setInfiniteConstraintBoundThreshold | ( | double | value | ) |
Constraint bounds with absolute value at least this threshold are replaced with infinities. NOTE: This primarily affects the relative convergence criteria. A smaller value makes the relative convergence criteria stronger. It also affects the problem statistics LOG()ed at the start of the run, and the default initial primal weight, since that is based on the norm of the bounds.
optional double infinite_constraint_bound_threshold = 22 [default = inf];
| value | The infiniteConstraintBoundThreshold to set. |
Definition at line 5145 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setInitialPrimalWeight | ( | double | value | ) |
The initial value of the primal weight (i.e., the ratio of primal and dual
step sizes). The primal weight remains fixed throughout the solve if
primal_weight_update_smoothing = 0.0. If unset, the default is the ratio of
the norm of the objective vector to the L2 norm of the combined constraint
bounds vector (as defined above). If this ratio is not finite and positive,
then the default is 1.0 instead. For tuning, try powers of 10, for example,
from 10^{-6} to 10^6.
optional double initial_primal_weight = 8;
| value | The initialPrimalWeight to set. |
Definition at line 4180 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setInitialStepSizeScaling | ( | double | value | ) |
Scaling factor applied to the initial step size (all step sizes if linesearch_rule == CONSTANT_STEP_SIZE_RULE).
optional double initial_step_size_scaling = 25 [default = 1];
| value | The initialStepSizeScaling to set. |
Definition at line 4926 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setL2NormRescaling | ( | boolean | value | ) |
If true, applies L_2 norm rescaling after the Ruiz rescaling. Heuristically this has been found to help convergence.
optional bool l2_norm_rescaling = 10 [default = true];
| value | The l2NormRescaling to set. |
Definition at line 4430 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setLinesearchRule | ( | com.google.ortools.pdlp.PrimalDualHybridGradientParams.LinesearchRule | value | ) |
Linesearch rule applied at each major iteration.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
| value | The linesearchRule to set. |
Definition at line 4623 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setLInfRuizIterations | ( | int | value | ) |
Number of L_infinity Ruiz rescaling iterations to apply to the constraint matrix. Zero disables this rescaling pass. Recommended values to try when tuning are 0, 5, and 10.
optional int32 l_inf_ruiz_iterations = 9 [default = 5];
| value | The lInfRuizIterations to set. |
Definition at line 4369 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setLogIntervalSeconds | ( | double | value | ) |
Time between iteration-level statistics logging (if `verbosity_level > 1`). Since iteration-level statistics are only generated when performing termination checks, logs will be generated from next termination check after `log_interval_seconds` have elapsed. Should be >= 0.0. 0.0 (the default) means log statistics at every termination check.
optional double log_interval_seconds = 31 [default = 0];
| value | The logIntervalSeconds to set. |
Definition at line 3812 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. Major iterations also trigger a termination check. For best performance using the NO_RESTARTS or EVERY_MAJOR_ITERATION rule, one should perform a log-scale grid search over this parameter, for example, over powers of two. ADAPTIVE_HEURISTIC is mostly insensitive to this value.
optional int32 major_iteration_frequency = 4 [default = 64];
| value | The majorIterationFrequency to set. |
Definition at line 3887 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setMalitskyPockParameters | ( | com.google.ortools.pdlp.MalitskyPockParams | value | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Definition at line 4792 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setMalitskyPockParameters | ( | com.google.ortools.pdlp.MalitskyPockParams.Builder | builderForValue | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Definition at line 4808 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. The value must be in the interval [sufficient_reduction_for_restart, 1). Smaller values make restarts less frequent, and larger values make them more frequent.
optional double necessary_reduction_for_restart = 17 [default = 0.9];
| value | The necessaryReductionForRestart to set. |
Definition at line 4563 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setNumShards | ( | int | value | ) |
For more efficient parallel computation, the matrices and vectors are divided (virtually) into num_shards shards. Results are computed independently for each shard and then combined. As a consequence, the order of computation, and hence floating point roundoff, depends on the number of shards so reproducible results require using the same value for num_shards. However, for efficiency num_shards should a be at least num_threads, and preferably at least 4*num_threads to allow better load balancing. If num_shards is positive, the computation will use that many shards. Otherwise a default that depends on num_threads will be used.
optional int32 num_shards = 27 [default = 0];
| value | The numShards to set. |
Definition at line 3572 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setNumThreads | ( | int | value | ) |
The number of threads to use. Must be positive. Try various values of num_threads, up to the number of physical cores. Performance may not be monotonically increasing with the number of threads because of memory bandwidth limitations.
optional int32 num_threads = 2 [default = 1];
| value | The numThreads to set. |
Definition at line 3489 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setPresolveOptions | ( | com.google.ortools.pdlp.PrimalDualHybridGradientParams.PresolveOptions | value | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Definition at line 4232 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setPresolveOptions | ( | com.google.ortools.pdlp.PrimalDualHybridGradientParams.PresolveOptions.Builder | builderForValue | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Definition at line 4248 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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). At 0.0, the primal weight will be frozen at its initial value and there will be no dynamic updates in the algorithm. At 1.0, there is no smoothing in the updates. The default of 0.5 generally performs well, but has been observed on occasion to trigger unstable swings in the primal weight. We recommend also trying 0.0 (disabling primal weight updates), in which case you must also tune initial_primal_weight.
optional double primal_weight_update_smoothing = 7 [default = 0.5];
| value | The primalWeightUpdateSmoothing to set. |
Definition at line 4099 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setRandomProjectionSeeds | ( | int | index, |
| int | value ) |
Seeds for generating (pseudo-)random projections of iterates during termination checks. For each seed, the projection of the primal and dual solutions onto random planes in primal and dual space will be computed and added the IterationStats if record_iteration_stats is true. The random planes generated will be determined by the seeds, the primal and dual dimensions, and num_threads.
repeated int32 random_projection_seeds = 28 [packed = true];
| index | The index to set the value at. |
| value | The randomProjectionSeeds to set. |
Definition at line 5022 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setRecordIterationStats | ( | boolean | value | ) |
If true, the iteration_stats field of the SolveLog output will be populated at every iteration. Note that we only compute solution statistics at termination checks. Setting this parameter to true may substantially increase the size of the output.
optional bool record_iteration_stats = 3;
| value | The recordIterationStats to set. |
Definition at line 3645 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setRestartStrategy | ( | com.google.ortools.pdlp.PrimalDualHybridGradientParams.RestartStrategy | value | ) |
NO_RESTARTS and EVERY_MAJOR_ITERATION occasionally outperform the default. If using a strategy other than ADAPTIVE_HEURISTIC, you must also tune major_iteration_frequency.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.RestartStrategy restart_strategy = 6 [default = ADAPTIVE_HEURISTIC];
| value | The restartStrategy to set. |
Definition at line 4018 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. Must be between 0.0 and 1.0.
optional double sufficient_reduction_for_restart = 11 [default = 0.1];
| value | The sufficientReductionForRestart to set. |
Definition at line 4493 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. Termination checks do not affect algorithmic progress unless termination is triggered.
optional int32 termination_check_frequency = 5 [default = 64];
| value | The terminationCheckFrequency to set. |
Definition at line 3954 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setTerminationCriteria | ( | com.google.ortools.pdlp.TerminationCriteria | value | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Definition at line 3349 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setTerminationCriteria | ( | com.google.ortools.pdlp.TerminationCriteria.Builder | builderForValue | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Definition at line 3365 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. The `diagonal_qp_solver_accuracy` parameter controls the solve accuracy. TODO(user): Turn this option on by default for quadratic programs after numerical evaluation.
optional bool use_diagonal_qp_trust_region_solver = 23 [default = false];
| value | The useDiagonalQpTrustRegionSolver to set. |
Definition at line 5285 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.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. This is useful primarily when the feasibility tolerances are fairly tight and the objective gap tolerance is somewhat looser. Note that this does not change the termination criteria, but rather can help achieve the termination criteria more quickly when the objective gap is not as important as feasibility. `use_feasibility_polishing` cannot be used with glop presolve, and requires `handle_some_primal_gradients_on_finite_bounds_as_residuals == false`. `use_feasibility_polishing` can only be used with linear programs. Feasibility polishing runs two separate phases, primal feasibility and dual feasibility. The primal feasibility phase runs PDHG on the primal feasibility problem (obtained by changing the objective vector to all zeros), using the average primal iterate and zero dual (which is optimal for the primal feasibility problem) as the initial solution. The dual feasibility phase runs PDHG on the dual feasibility problem (obtained by changing all finite variable and constraint bounds to zero), using the average dual iterate and zero primal (which is optimal for the dual feasibility problem) as the initial solution. The primal solution from the primal feasibility phase and dual solution from the dual feasibility phase are then combined (forming a solution of type `POINT_TYPE_FEASIBILITY_POLISHING_SOLUTION`) and checked against the termination criteria.
optional bool use_feasibility_polishing = 30 [default = false];
| value | The useFeasibilityPolishing to set. |
Definition at line 5493 of file PrimalDualHybridGradientParams.java.
| Builder com.google.ortools.pdlp.PrimalDualHybridGradientParams.Builder.setVerbosityLevel | ( | int | value | ) |
The verbosity of logging. 0: No informational logging. (Errors are logged.) 1: Summary statistics only. No iteration-level details. 2: A table of iteration-level statistics is logged. (See ToShortString() in primal_dual_hybrid_gradient.cc). 3: A more detailed table of iteration-level statistics is logged. (See ToString() in primal_dual_hybrid_gradient.cc). 4: For iteration-level details, prints the statistics of both the average (prefixed with A) and the current iterate (prefixed with C). Also prints internal algorithmic state and details. Logging at levels 2-4 also includes messages from level 1.
optional int32 verbosity_level = 26 [default = 0];
| value | The verbosityLevel to set. |
Definition at line 3734 of file PrimalDualHybridGradientParams.java.
1.12.0