ISRES#

class ISRES(max_evals=1000)[source]#

Bases: NLoptOptimizer

Improved Stochastic Ranking Evolution Strategy optimizer.

Improved Stochastic Ranking Evolution Strategy (ISRES) is an algorithm for non-linearly constrained global optimization. It has heuristics to escape local optima, even though convergence to a global optima is not guaranteed. The evolution strategy is based on a combination of a mutation rule and differential variation. The fitness ranking is simply via the objective function for problems without nonlinear constraints. When nonlinear constraints are included, the stochastic ranking proposed by Runarsson and Yao is employed. This method supports arbitrary nonlinear inequality and equality constraints, in addition to the bound constraints.

NLopt global optimizer, derivative-free. For further detail, please refer to http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#isres-improved-stochastic-ranking-evolution-strategy

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[[POINT], float]) – The scalar function to minimize.
x0 (POINT) – The initial point for the minimization.
jac (Callable[[POINT], POINT] | 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