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Google OR-Tools v9.9
a fast and portable software suite for combinatorial optimization
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Google OR-Tools v9.9
a fast and portable software suite for combinatorial optimization
|
Definition at line 5933 of file Solvers.java.
| operations_research.pdlp.Solvers.AdaptiveLinesearchParams operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getAdaptiveLinesearchParameters | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.AdaptiveLinesearchParamsOrBuilder operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getAdaptiveLinesearchParametersOrBuilder | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.LinesearchRule operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getLinesearchRule | ( | ) |
Linesearch rule applied at each major iteration.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.MalitskyPockParams operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getMalitskyPockParameters | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.MalitskyPockParamsOrBuilder operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getMalitskyPockParametersOrBuilder | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.PresolveOptions operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getPresolveOptions | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.PresolveOptionsOrBuilder operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getPresolveOptionsOrBuilder | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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. |
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| java.util.List< java.lang.Integer > operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.RestartStrategy operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| double operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.TerminationCriteria operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getTerminationCriteria | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| operations_research.pdlp.Solvers.TerminationCriteriaOrBuilder operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.getTerminationCriteriaOrBuilder | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| int operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.hasAdaptiveLinesearchParameters | ( | ) |
optional .operations_research.pdlp.AdaptiveLinesearchParams adaptive_linesearch_parameters = 18;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.hasLinesearchRule | ( | ) |
Linesearch rule applied at each major iteration.
optional .operations_research.pdlp.PrimalDualHybridGradientParams.LinesearchRule linesearch_rule = 12 [default = ADAPTIVE_LINESEARCH_RULE];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.hasMalitskyPockParameters | ( | ) |
optional .operations_research.pdlp.MalitskyPockParams malitsky_pock_parameters = 19;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.hasPresolveOptions | ( | ) |
optional .operations_research.pdlp.PrimalDualHybridGradientParams.PresolveOptions presolve_options = 16;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.hasTerminationCriteria | ( | ) |
optional .operations_research.pdlp.TerminationCriteria termination_criteria = 1;
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
| boolean operations_research.pdlp.Solvers.PrimalDualHybridGradientParamsOrBuilder.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];
Implemented in operations_research.pdlp.Solvers.PrimalDualHybridGradientParams, and operations_research.pdlp.Solvers.PrimalDualHybridGradientParams.Builder.
1.10.0