discrete_optimization.generic_tools.ls package

Submodules

discrete_optimization.generic_tools.ls.hill_climber module

class discrete_optimization.generic_tools.ls.hill_climber.HillClimber(problem: Problem, mutator: Mutation, restart_handler: RestartHandler, mode_mutation: ModeMutation, params_objective_function: ParamsObjectiveFunction | None = None, store_solution: bool = False)[source]

Bases: SolverDO

solve(initial_variable: Solution, nb_iteration_max: int, callbacks: List[Callback] | None = None, **kwargs: Any) → ResultStorage[source]

Generic solving function.

Parameters:

  • callbacks – list of callbacks used to hook into the various stage of the solve

  • **kwargs – any argument specific to the solver

Solvers deriving from SolverDo should use callbacks methods .on_step_end(), … during solve(). But some solvers are not yet updated and are just ignoring it.

Returns (ResultStorage): a result object containing potentially a pool of solutions to a discrete-optimization problem

class discrete_optimization.generic_tools.ls.hill_climber.HillClimberPareto(problem: Problem, mutator: Mutation, restart_handler: RestartHandler, mode_mutation: ModeMutation, params_objective_function: ParamsObjectiveFunction | None = None, store_solution: bool = False)[source]

Bases: HillClimber

solve(initial_variable: Solution, nb_iteration_max: int, update_iteration_pareto: int = 1000, callbacks: List[Callback] | None = None, **kwargs: Any) → ParetoFront[source]

Generic solving function.

Parameters:

  • callbacks – list of callbacks used to hook into the various stage of the solve

  • **kwargs – any argument specific to the solver

Solvers deriving from SolverDo should use callbacks methods .on_step_end(), … during solve(). But some solvers are not yet updated and are just ignoring it.

Returns (ResultStorage): a result object containing potentially a pool of solutions to a discrete-optimization problem

discrete_optimization.generic_tools.ls.simulated_annealing module

class discrete_optimization.generic_tools.ls.simulated_annealing.SimulatedAnnealing(problem: Problem, mutator: Mutation, restart_handler: RestartHandler, temperature_handler: TemperatureScheduling, mode_mutation: ModeMutation, params_objective_function: ParamsObjectiveFunction | None = None, store_solution: bool = False)[source]

Bases: SolverDO

aggreg_from_dict: Callable[[Dict[str, float]], float]
aggreg_from_sol: Callable[[Solution], float]
solve(initial_variable: Solution, nb_iteration_max: int, callbacks: List[Callback] | None = None, **kwargs: Any) → ResultStorage[source]

Generic solving function.

Parameters:

  • callbacks – list of callbacks used to hook into the various stage of the solve

  • **kwargs – any argument specific to the solver

Solvers deriving from SolverDo should use callbacks methods .on_step_end(), … during solve(). But some solvers are not yet updated and are just ignoring it.

Returns (ResultStorage): a result object containing potentially a pool of solutions to a discrete-optimization problem

class discrete_optimization.generic_tools.ls.simulated_annealing.TemperatureScheduling[source]

Bases: object

nb_iteration: int
abstract next_temperature() → float[source]
restart_handler: RestartHandler
temperature: float
class discrete_optimization.generic_tools.ls.simulated_annealing.TemperatureSchedulingFactor(temperature: float, restart_handler: RestartHandler, coefficient: float = 0.99)[source]

Bases: TemperatureScheduling

next_temperature() → float[source]

Module contents