IMFIL#

class qiskit.algorithms.optimizers.IMFIL(maxiter=1000)[소스]#

기반 클래스: Optimizer

IMplicit FILtering algorithm.

Implicit filtering is a way to solve bound-constrained optimization problems for which derivatives are not available. In comparison to methods that use interpolation to reconstruct the function and its higher derivatives, implicit filtering builds upon coordinate search followed by interpolation to get an approximate gradient.

Uses skquant.opt installed with pip install scikit-quant. For further detail, please refer to https://github.com/scikit-quant/scikit-quant and https://qat4chem.lbl.gov/software.

매개변수:: maxiter (int) – Maximum number of function evaluations.
예외 발생:: MissingOptionalLibraryError – scikit-quant 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_support_level()[소스]#: Returns 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.

매개변수:

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).

반환:

the gradient computed

반환 형식:

grad

minimize(fun, x0, jac=None, bounds=None)[소스]#

Minimize the scalar function.

매개변수:

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.

반환:

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

반환 형식:

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.

매개변수:: 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.

매개변수:

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

반환:

wrapper

반환 형식:

function_wrapper