downhill_simplex module

Define the Downhill simplex (or Nelder-Mead) algorihm.

class DownhillSimplex(*, compensating_elements, elts, objectives, variables, compute_beam_propagation, compute_residuals, cavity_settings_factory, reference_simulation_output, constraints=None, compute_constraints=None, optimisation_algorithm_kwargs=None, history_kwargs=None, **kwargs)

Bases: OptimisationAlgorithm

Downhill simplex method, which does not use derivatives.

All the attributes but solution are inherited from the Abstract Base Class OptimisationAlgorithm.

See also

DownhillSimplexPenalty

Parameters:

• compensating_elements (Collection[Element])

• elts (ListOfElements)

• objectives (Collection[Objective])

• variables (Collection[Variable])

• compute_beam_propagation (Callable[[SetOfCavitySettings], SimulationOutput])

• compute_residuals (Callable[[SimulationOutput], Any])

• cavity_settings_factory (CavitySettingsFactory)

• reference_simulation_output (SimulationOutput)

• constraints (Collection[Constraint] | None, default: None)

• compute_constraints (Callable[[SimulationOutput], ndarray] | None, default: None)

• optimisation_algorithm_kwargs (dict[str, Any] | None, default: None)

• history_kwargs (dict[str, Any] | None, default: None)

supports_constraints: bool = False

optimize()

Set up the optimization and solve the problem.

Returns:

opti_sol – Gives list of solutions, corresponding objective, convergence violation if applicable, etc.

Return type:

OptiSol

property _default_kwargs: dict[str, Any]

Create the kwargs for the optimisation.

_generate_opti_sol(result)

Store the optimization results.

Parameters:

result (OptimizeResult)

Return type:

OptiSol

_format_variables()

Convert the Variable to an array and Bounds.

Return type:

tuple[ndarray, Bounds]

_abc_impl = <_abc._abc_data object at 0x73dca6b67600>