CRS¶

class CRS(max_evals=1000)[source]¶

Bases: NLoptOptimizer

Controlled Random Search (CRS) with local mutation optimizer.

Controlled Random Search (CRS) with local mutation is part of the family of the CRS optimizers. The CRS optimizers start with a random population of points, and randomly evolve these points by heuristic rules. In the case of CRS with local mutation, the evolution is a randomized version of the NELDER_MEAD local optimizer.

NLopt global optimizer, derivative-free. For further detail, please refer to https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#controlled-random-search-crs-with-local-mutation

Parameters:: max_evals (int) – Maximum allowed number of function evaluations.
Raises:: MissingOptionalLibraryError – NLopt library not installed.

Attributes

bounds_support_level¶: Returns bounds support level

gradient_support_level¶: Returns gradient support level

initial_point_support_level¶: Returns initial point support level

is_bounds_ignored¶: Returns is bounds ignored

is_bounds_required¶: Returns is bounds required

is_bounds_supported¶: Returns is bounds supported

is_gradient_ignored¶: Returns is gradient ignored

is_gradient_required¶: Returns is gradient required

is_gradient_supported¶: Returns is gradient supported

is_initial_point_ignored¶: Returns is initial point ignored

is_initial_point_required¶: Returns is initial point required

is_initial_point_supported¶: Returns is initial point supported

setting¶: Return setting

settings¶

Methods

get_nlopt_optimizer()[source]¶

Return NLopt optimizer type

Return type:: NLoptOptimizerType

get_support_level()¶: return support level dictionary

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)¶

We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.

Parameters:

x_center (ndarray) – point around which we compute the gradient
f (func) – the function of which the gradient is to be computed.
epsilon (float) – the epsilon used in the numeric differentiation.
max_evals_grouped (int) – max evals grouped, defaults to 1 (i.e. no batching).

Returns:

the gradient computed

Return type:

grad

minimize(fun, x0, jac=None, bounds=None)¶

Minimize the scalar function.

Parameters:

fun (Callable[[float | ndarray], float]) – The scalar function to minimize.
x0 (float | ndarray) – The initial point for the minimization.
jac (Callable[[float | ndarray], float | ndarray] | None) – The gradient of the scalar function fun.
bounds (list[tuple[float, float]] | None) – Bounds for the variables of fun. This argument might be ignored if the optimizer does not support bounds.

Returns:

The result of the optimization, containing e.g. the result as attribute x.

Return type:

OptimizerResult

print_options()¶: Print algorithm-specific options.

set_max_evals_grouped(limit)¶: Set max evals grouped

set_options(**kwargs)¶

Sets or updates values in the options dictionary.

The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.

Parameters:: kwargs (dict) – options, given as name=value.

static wrap_function(function, args)¶

Wrap the function to implicitly inject the args at the call of the function.

Parameters:

function (func) – the target function
args (tuple) – the args to be injected

Returns:

wrapper

Return type:

function_wrapper