DIRECT_L#

class DIRECT_L(max_evals=1000)[source]#

Bases: NLoptOptimizer

DIviding RECTangles Locally-biased optimizer.

DIviding RECTangles (DIRECT) is a deterministic-search algorithms based on systematic division of the search domain into increasingly smaller hyper-rectangles. The DIRECT-L version is a “locally biased” variant of DIRECT that makes the algorithm more biased towards local search, so that it is more efficient for functions with few local minima.

NLopt global optimizer, derivative-free. For further detail, please refer to http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#direct-and-direct-l

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