parameters.proto

Go to the documentation of this file.

// Copyright 2010-2025 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
 
// Configures the behavior of a MathOpt solver.
 
syntax = "proto3";
 
package operations_research.service.v1.mathopt;
 
import "google/protobuf/duration.proto";
 
option java_multiple_files = true;
option java_package = "com.google.ortools.service.v1.mathopt";
 
option csharp_namespace = "Google.OrTools.Service";
 
// The solvers supported by MathOpt.
enum SolverTypeProto {
  SOLVER_TYPE_UNSPECIFIED = 0;
 
  // Solving Constraint Integer Programs (SCIP) solver (third party).
  //
  // Supports LP, MIP, and nonconvex integer quadratic problems. No dual data
  // for LPs is returned though. Prefer GLOP for LPs.
  SOLVER_TYPE_GSCIP = 1;
 
  // Gurobi solver (third party).
  //
  // Supports LP, MIP, and nonconvex integer quadratic problems. Generally the
  // fastest option, but has special licensing.
  SOLVER_TYPE_GUROBI = 2;
 
  // Google's Glop solver.
  //
  // Supports LP with primal and dual simplex methods.
  SOLVER_TYPE_GLOP = 3;
 
  // Google's CP-SAT solver.
  //
  // Supports problems where all variables are integer and bounded (or implied
  // to be after presolve). Experimental support to rescale and discretize
  // problems with continuous variables.
  SOLVER_TYPE_CP_SAT = 4;
 
  // Google's PDLP solver.
  //
  // Supports LP and convex diagonal quadratic objectives. Uses first order
  // methods rather than simplex. Can solve very large problems.
  SOLVER_TYPE_PDLP = 5;
 
  // GNU Linear Programming Kit (GLPK) (third party).
  //
  // Supports MIP and LP.
  //
  // Thread-safety: GLPK use thread-local storage for memory allocations. As a
  // consequence Solver instances must be destroyed on the same thread as they
  // are created or GLPK will crash. It seems OK to call Solver::Solve() from
  // another thread than the one used to create the Solver but it is not
  // documented by GLPK and should be avoided.
  //
  // When solving a LP with the presolver, a solution (and the unbound rays) are
  // only returned if an optimal solution has been found. Else nothing is
  // returned. See glpk-5.0/doc/glpk.pdf page #40 available from glpk-5.0.tar.gz
  // for details.
  SOLVER_TYPE_GLPK = 6;
 
  // The Operator Splitting Quadratic Program (OSQP) solver (third party).
  //
  // Supports continuous problems with linear constraints and linear or convex
  // quadratic objectives. Uses a first-order method.
  SOLVER_TYPE_OSQP = 7;
 
  // The Embedded Conic Solver (ECOS) (third party).
  //
  // Supports LP and SOCP problems. Uses interior point methods (barrier).
  SOLVER_TYPE_ECOS = 8;
 
  // The Splitting Conic Solver (SCS) (third party).
  //
  // Supports LP and SOCP problems. Uses a first-order method.
  SOLVER_TYPE_SCS = 9;
 
  // The HiGHS Solver (third party).
  //
  // Supports LP and MIP problems (convex QPs are unimplemented).
  SOLVER_TYPE_HIGHS = 10;
 
  // MathOpt's reference implementation of a MIP solver.
  //
  // Slow/not recommended for production. Not an LP solver (no dual information
  // returned).
  SOLVER_TYPE_SANTORINI = 11;
 
  // Fico XPRESS solver (third party).
  //
  // Supports LP, MIP, and nonconvex integer quadratic problems.
  // A fast option, but has special licensing.
  SOLVER_TYPE_XPRESS = 12;
}
 
// Selects an algorithm for solving linear programs.
enum LPAlgorithmProto {
  LP_ALGORITHM_UNSPECIFIED = 0;
 
  // The (primal) simplex method. Typically can provide primal and dual
  // solutions, primal/dual rays on primal/dual unbounded problems, and a basis.
  LP_ALGORITHM_PRIMAL_SIMPLEX = 1;
 
  // The dual simplex method. Typically can provide primal and dual
  // solutions, primal/dual rays on primal/dual unbounded problems, and a basis.
  LP_ALGORITHM_DUAL_SIMPLEX = 2;
 
  // The barrier method, also commonly called an interior point method (IPM).
  // Can typically give both primal and dual solutions. Some implementations can
  // also produce rays on unbounded/infeasible problems. A basis is not given
  // unless the underlying solver does "crossover" and finishes with simplex.
  LP_ALGORITHM_BARRIER = 3;
 
  // An algorithm based around a first-order method. These will typically
  // produce both primal and dual solutions, and potentially also certificates
  // of primal and/or dual infeasibility. First-order methods typically will
  // provide solutions with lower accuracy, so users should take care to set
  // solution quality parameters (e.g., tolerances) and to validate solutions.
  LP_ALGORITHM_FIRST_ORDER = 4;
}
 
// Effort level applied to an optional task while solving (see
// SolveParametersProto for use).
//
// Emphasis is used to configure a solver feature as follows:
//  * If a solver doesn't support the feature, only UNSPECIFIED will always be
//    valid, any other setting will typically an invalid argument error (some
//    solvers may also accept OFF).
//  * If the solver supports the feature:
//    - When set to UNSPECIFIED, the underlying default is used.
//    - When the feature cannot be turned off, OFF will return an error.
//    - If the feature is enabled by default, the solver default is typically
//      mapped to MEDIUM.
//    - If the feature is supported, LOW, MEDIUM, HIGH, and VERY HIGH will never
//      give an error, and will map onto their best match.
enum EmphasisProto {
  EMPHASIS_UNSPECIFIED = 0;
  EMPHASIS_OFF = 1;
  EMPHASIS_LOW = 2;
  EMPHASIS_MEDIUM = 3;
  EMPHASIS_HIGH = 4;
  EMPHASIS_VERY_HIGH = 5;
}
 
// Parameters to control a single solve.
//
// Contains both parameters common to all solvers e.g. time_limit, and
// parameters for a specific solver, e.g. gscip. If a value is set in both
// common and solver specific field, the solver specific setting is used.
//
// The common parameters that are optional and unset or an enum with value
// unspecified indicate that the solver default is used.
//
// Solver specific parameters for solvers other than the one in use are ignored.
//
// Parameters that depends on the model (e.g. branching priority is set for
// each variable) are passed in ModelSolveParametersProto.
message SolveParametersProto {
  // Maximum time a solver should spend on the problem (or infinite if not set).
  //
  // This value is not a hard limit, solve time may slightly exceed this value.
  // This parameter is always passed to the underlying solver, the solver
  // default is not used.
  google.protobuf.Duration time_limit = 1;
 
  // Limit on the iterations of the underlying algorithm (e.g. simplex pivots).
  // The specific behavior is dependent on the solver and algorithm used, but
  // often can give a deterministic solve limit (further configuration may be
  // needed, e.g. one thread).
  //
  // Typically supported by LP, QP, and MIP solvers, but for MIP solvers see
  // also node_limit.
  optional int64 iteration_limit = 2;
 
  // Limit on the number of subproblems solved in enumerative search (e.g.
  // branch and bound). For many solvers this can be used to deterministically
  // limit computation (further configuration may be needed, e.g. one thread).
  //
  // Typically for MIP solvers, see also iteration_limit.
  optional int64 node_limit = 24;
 
  // The solver stops early if it can prove there are no primal solutions at
  // least as good as cutoff.
  //
  // On an early stop, the solver returns termination reason NO_SOLUTION_FOUND
  // and with limit CUTOFF and is not required to give any extra solution
  // information. Has no effect on the return value if there is no early stop.
  //
  // It is recommended that you use a tolerance if you want solutions with
  // objective exactly equal to cutoff to be returned.
  //
  // See the user guide for more details and a comparison with best_bound_limit.
  optional double cutoff_limit = 20;
 
  // The solver stops early as soon as it finds a solution at least this good,
  // with termination reason FEASIBLE and limit OBJECTIVE.
  optional double objective_limit = 21;
 
  // The solver stops early as soon as it proves the best bound is at least this
  // good, with termination reason FEASIBLE or NO_SOLUTION_FOUND and limit
  // OBJECTIVE.
  //
  // See the user guide for more details and a comparison with cutoff_limit.
  optional double best_bound_limit = 22;
 
  // The solver stops early after finding this many feasible solutions, with
  // termination reason FEASIBLE and limit SOLUTION. Must be greater than zero
  // if set. It is often used get the solver to stop on the first feasible
  // solution found. Note that there is no guarantee on the objective value for
  // any of the returned solutions.
  //
  // Solvers will typically not return more solutions than the solution limit,
  // but this is not enforced by MathOpt, see also b/214041169.
  //
  // Currently supported for Gurobi and SCIP, and for CP-SAT only with value 1.
  optional int32 solution_limit = 23;
 
  // Enables printing the solver implementation traces. The location of those
  // traces depend on the solver. For SCIP and Gurobi this will be the standard
  // output streams. For Glop and CP-SAT this will LOG(INFO).
  //
  // Note that if the solver supports message callback and the user registers a
  // callback for it, then this parameter value is ignored and no traces are
  // printed.
  bool enable_output = 3;
 
  // If set, it must be >= 1.
  optional int32 threads = 4;
 
  // Seed for the pseudo-random number generator in the underlying
  // solver. Note that all solvers use pseudo-random numbers to select things
  // such as perturbation in the LP algorithm, for tie-break-up rules, and for
  // heuristic fixings. Varying this can have a noticeable impact on solver
  // behavior.
  //
  // Although all solvers have a concept of seeds, note that valid values
  // depend on the actual solver.
  // - Gurobi: [0:GRB_MAXINT] (which as of Gurobi 9.0 is 2x10^9).
  // - GSCIP:  [0:2147483647] (which is MAX_INT or kint32max or 2^31-1).
  // - GLOP:   [0:2147483647] (same as above)
  // In all cases, the solver will receive a value equal to:
  // MAX(0, MIN(MAX_VALID_VALUE_FOR_SOLVER, random_seed)).
  optional int32 random_seed = 5;
 
  // An absolute optimality tolerance (primarily) for MIP solvers.
  //
  // The absolute GAP is the absolute value of the difference between:
  //   * the objective value of the best feasible solution found,
  //   * the dual bound produced by the search.
  // The solver can stop once the absolute GAP is at most absolute_gap_tolerance
  // (when set), and return TERMINATION_REASON_OPTIMAL.
  //
  // Must be >= 0 if set.
  //
  // See also relative_gap_tolerance.
  optional double absolute_gap_tolerance = 18;
 
  // A relative optimality tolerance (primarily) for MIP solvers.
  //
  // The relative GAP is a normalized version of the absolute GAP (defined on
  // absolute_gap_tolerance), where the normalization is solver-dependent, e.g.
  // the absolute GAP divided by the objective value of the best feasible
  // solution found.
  //
  // The solver can stop once the relative GAP is at most relative_gap_tolerance
  // (when set), and return TERMINATION_REASON_OPTIMAL.
  //
  // Must be >= 0 if set.
  //
  // See also absolute_gap_tolerance.
  optional double relative_gap_tolerance = 17;
 
  // Maintain up to `solution_pool_size` solutions while searching. The solution
  // pool generally has two functions:
  //  (1) For solvers that can return more than one solution, this limits how
  //      many solutions will be returned.
  //  (2) Some solvers may run heuristics using solutions from the solution
  //      pool, so changing this value may affect the algorithm's path.
  // To force the solver to fill the solution pool, e.g. with the n best
  // solutions, requires further, solver specific configuration.
  optional int32 solution_pool_size = 25;
 
  // The algorithm for solving a linear program. If LP_ALGORITHM_UNSPECIFIED,
  // use the solver default algorithm.
  //
  // For problems that are not linear programs but where linear programming is
  // a subroutine, solvers may use this value. E.g. MIP solvers will typically
  // use this for the root LP solve only (and use dual simplex otherwise).
  LPAlgorithmProto lp_algorithm = 6;
 
  // Effort on simplifying the problem before starting the main algorithm, or
  // the solver default effort level if EMPHASIS_UNSPECIFIED.
  EmphasisProto presolve = 7;
 
  // Effort on getting a stronger LP relaxation (MIP only), or the solver
  // default effort level if EMPHASIS_UNSPECIFIED.
  //
  // NOTE: disabling cuts may prevent callbacks from having a chance to add cuts
  // at MIP_NODE, this behavior is solver specific.
  EmphasisProto cuts = 8;
 
  // Effort in finding feasible solutions beyond those encountered in the
  // complete search procedure (MIP only), or the solver default effort level if
  // EMPHASIS_UNSPECIFIED.
  EmphasisProto heuristics = 9;
 
  // Effort in rescaling the problem to improve numerical stability, or the
  // solver default effort level if EMPHASIS_UNSPECIFIED.
  EmphasisProto scaling = 10;
 
  reserved 12, 13, 14, 15, 16, 19, 26, 27;
 
  reserved 11;  // Deleted
}