Quellcode für qiskit.algorithms.optimizers.imfil

# This code is part of Qiskit.
# (C) Copyright IBM 2019, 2020.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""IMplicit FILtering (IMFIL) optimizer."""
from __future__ import annotations

from collections.abc import Callable
from typing import Any

from qiskit.utils import optionals as _optionals
from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT

[Doku]@_optionals.HAS_SKQUANT.require_in_instance class IMFIL(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. """ def __init__( self, maxiter: int = 1000, ) -> None: """ Args: maxiter: Maximum number of function evaluations. Raises: MissingOptionalLibraryError: scikit-quant not installed """ super().__init__() self._maxiter = maxiter
[Doku] def get_support_level(self): """Returns support level dictionary.""" return { "gradient": OptimizerSupportLevel.ignored, "bounds": OptimizerSupportLevel.required, "initial_point": OptimizerSupportLevel.required, }
@property def settings(self) -> dict[str, Any]: return { "maxiter": self._maxiter, }
[Doku] def minimize( self, fun: Callable[[POINT], float], x0: POINT, jac: Callable[[POINT], POINT] | None = None, bounds: list[tuple[float, float]] | None = None, ) -> OptimizerResult: from skquant import opt as skq res, history = skq.minimize( func=fun, x0=x0, bounds=bounds, budget=self._maxiter, method="imfil", ) optimizer_result = OptimizerResult() optimizer_result.x = res.optpar optimizer_result.fun = res.optval optimizer_result.nfev = len(history) return optimizer_result