explorator module

Define Explorator, a module to explore the design space.

In order to be consistent with the ABC OptimisationAlgorithm, it also returns the solution with the lowest residual value – hence it is also a “brute-force” optimisation algorithm.

Todo

Make this class more robust. In particular: save all objectives (not just the norm), handle export when there is more than two variables, also save complementary data (e.g.: always save phi_s even it is not in the constraints nor variables).

Todo

Allow for different number of points according to variable.

class Explorator(*, 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

Method that tries all the possible solutions.

Notes

Very inefficient for optimization. It is however useful to study a specific case.

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

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 = True

compute_constraints: Callable[[SimulationOutput], ndarray]

optimize()

Set up the optimization and solve the problem.

Return type:

OptiSol

Returns:

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

_algorithm_parameters()

Create the kwargs for the optimisation.

Return type:

dict

_generate_combinations(n_points=10, **kwargs)

Generate all the possible combinations of the variables.

Parameters:

n_points (int, default: 10)

Return type:

tuple[ndarray, ndarray]

_array_of_values_to_mesh(objectives_values, n_points=10, **kwargs)

Reformat the results for plotting purposes.

Parameters:

• objectives_values (ndarray)

• n_points (int, default: 10)

Return type:

ndarray

_generate_opti_sol(variables_values, objectives_values, criterion)

Create the dictionary holding all relatable information.

Parameters:

• variables_values (ndarray)

• objectives_values (ndarray)

• criterion (Literal['minimize norm of objective'])

Return type:

OptiSol

_take_best_solution(variable_comb, objectives_values, criterion)

Take the “best” of the calculated solutions.

Parameters:

• variable_comb (ndarray) – All the set of variables (cavity parameters) that were tried.

• objectives_values (ndarray) – The values of the objective corresponding to variable_comb.

• criterion (Literal['minimize norm of objective']) – Name of the criterion that will determine which solution is the “best”. Only one is implemented for now, may add others in the future.

Return type:

tuple[ndarray | None, ndarray | None]

Returns:

• best_solution – “Best” solution.

• best_objective – Objective values corresponding to best_solution.

_abc_impl = <_abc._abc_data object at 0x75e515324d40>