discrete_optimization.generic_tools.hub_solver.optal package

Submodules

discrete_optimization.generic_tools.hub_solver.optal.optalcp_tools module

class discrete_optimization.generic_tools.hub_solver.optal.optalcp_tools.OptalCpSolver(problem: Problem, params_objective_function: ParamsObjectiveFunction | None = None, **kwargs: Any)[source]

Bases: CpSolver, BoundsProviderMixin

add_bound_constraint(var: Any, sign: SignEnum, value: int) → list[Any][source]

Add constraint of bound type on an integer variable (or expression) of the underlying cp model.

var must compare to value according to value.

Parameters:

  • var

  • sign

  • value

Returns:

cp_model: cp.Model | None = None
current_bound: int | float | None = None
current_obj: int | float | None = None
early_stopping_exception: Exception | None = None
get_current_best_internal_objective_bound() → float | None[source]
get_current_best_internal_objective_value() → float | None[source]
hyperparameters = [CategoricalHyperparameter(name='searchType', default='Auto', depends_on=None, name_in_kwargs='searchType')]

Hyperparameters available for this solver.

These hyperparameters are to be feed to **kwargs found in

  • __init__()

  • init_model() (when available)

  • solve()

minimize_variable(var: Any) → None[source]

Set the cp solver objective as minimizing var.

abstractmethod retrieve_solution(result: cp.SolveResult) → Solution[source]

Return a d-o solution from the variables computed by minizinc.

Parameters:

result – output of the cp.solve

Returns:

solve(callbacks: list[Callback] | None = None, time_limit: int | None = 60, parameters_cp: ParametersCp | None = None, do_not_retrieve_solutions: bool = False, **args: Any) → ResultStorage[source]

Solve the cp model

Parameters:

  • callbacks

  • time_limit

  • parameters_cp

  • do_not_retrieve_solutions – if True, do no try to retrieve solutions. Useful when using preview version of optalcp.

  • **args

Returns:

status_solver: StatusSolver = None
use_warm_start: bool = False
warm_start_solution: cp.Solution | None = None
class discrete_optimization.generic_tools.hub_solver.optal.optalcp_tools.OptalSolutionCallback(do_solver: OptalCpSolver, optal_solver: cp.Solver, callback: Callback, do_not_retrieve_solutions: bool = False)[source]

Bases: object

current_solve_result: cp.SolveResult | None
on_bound(event: cp.ObjectiveBoundEntry) → None[source]
on_solution(solution: cp.SolutionEvent) → None[source]
discrete_optimization.generic_tools.hub_solver.optal.optalcp_tools.run_async_synchronously(coroutine: Awaitable[T]) → T[source]

Runs a coroutine synchronously, blocking until it completes.

This helper handles two cases: 1. No event loop is running (standard script): Uses asyncio.run() 2. An event loop is running (e.g., Jupyter): Runs the coroutine in a

separate thread to avoid blocking the existing loop/raising runtime errors.

Module contents