# IMFIL#

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

Bases: `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.

প্যারামিটার:

maxiter (int) -- Maximum number of function evaluations.

রেইজেস:

MissingOptionalLibraryError -- scikit-quant not installed

Attributes

bounds_support_level#

Returns bounds 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

Returns is gradient ignored

Returns is gradient required

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()[source]#

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.

প্যারামিটার:
• 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).

রিটার্নস:

রিটার্ন টাইপ:

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

Minimize the scalar function.

প্যারামিটার:
• 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.

রিটার্নস:

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

রিটার্ন টাইপ:

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.

প্যারামিটার:

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.

প্যারামিটার:
• function (func) -- the target function

• args (tuple) -- the args to be injected

রিটার্নস:

wrapper

রিটার্ন টাইপ:

function_wrapper