Parameters to control a single solve.
If a value is set in both common and solver specific field (e.g. gscip), the
solver specific setting 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 ModelSolveParameters.
See solve() and IncrementalSolver.solve() in solve.py for more details.
Attributes:
time_limit: The maximum time a solver should spend on the problem, or if
None, then the time limit is infinite. 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.
iteration_limit: 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.
node_limit: 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.
cutoff_limit: 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
TerminationReason.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.
objective_limit: The solver stops early as soon as it finds a solution at
least this good, with TerminationReason.FEASIBLE and Limit.OBJECTIVE.
best_bound_limit: The solver stops early as soon as it proves the best bound
is at least this good, with TerminationReason of FEASIBLE or
NO_SOLUTION_FOUND and Limit.OBJECTIVE. See the user guide for more details
and a comparison with cutoff_limit.
solution_limit: The solver stops early after finding this many feasible
solutions, with TerminationReason.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.
enable_output: If the solver should print out its log messages.
threads: An integer >= 1, how many threads to use when solving.
random_seed: Seed for the pseudo-random number generator in the underlying
solver. 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)).
absolute_gap_tolerance: 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 TerminationReason.OPTIMAL.
Must be >= 0 if set. See also relative_gap_tolerance.
relative_gap_tolerance: 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
TerminationReason.OPTIMAL. Must be >= 0 if set. See also
absolute_gap_tolerance.
solution_pool_size: Maintain up to `solution_pool_size` solutions while
searching. The solution pool generally has two functions:
* For solvers that can return more than one solution, this limits how
many solutions will be returned.
* 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.
lp_algorithm: The algorithm for solving a linear program. If 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).
presolve: Effort on simplifying the problem before starting the main
algorithm (e.g. simplex).
cuts: Effort on getting a stronger LP relaxation (MIP only). Note that in
some solvers, disabling cuts may prevent callbacks from having a chance to
add cuts at MIP_NODE.
heuristics: Effort in finding feasible solutions beyond those encountered in
the complete search procedure.
scaling: Effort in rescaling the problem to improve numerical stability.
gscip: GSCIP specific solve parameters.
gurobi: Gurobi specific solve parameters.
glop: Glop specific solve parameters.
cp_sat: CP-SAT specific solve parameters.
pdlp: PDLP specific solve parameters.
osqp: OSQP specific solve parameters. Users should prefer the generic
MathOpt parameters over OSQP-level parameters, when available: - Prefer
SolveParameters.enable_output to OsqpSettingsProto.verbose. - Prefer
SolveParameters.time_limit to OsqpSettingsProto.time_limit. - Prefer
SolveParameters.iteration_limit to OsqpSettingsProto.iteration_limit. - If
a less granular configuration is acceptable, prefer
SolveParameters.scaling to OsqpSettingsProto.
glpk: GLPK specific solve parameters.
highs: HiGHS specific solve parameters.
Definition at line 269 of file parameters.py.
1.13.2