Quellcode für qiskit.algorithms.optimizers.bobyqa

# 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.

"""Bound Optimization BY Quadratic Approximation (BOBYQA) optimizer."""

from __future__ import annotations

from collections.abc import Callable
from typing import Any

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

[Doku]@_optionals.HAS_SKQUANT.require_in_instance class BOBYQA(Optimizer): """Bound Optimization BY Quadratic Approximation algorithm. BOBYQA finds local solutions to nonlinear, non-convex minimization problems with optional bound constraints, without requirement of derivatives of the objective function. 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=np.asarray(x0), bounds=np.array(bounds), budget=self._maxiter, method="bobyqa", ) optimizer_result = OptimizerResult() optimizer_result.x = res.optpar optimizer_result.fun = res.optval optimizer_result.nfev = len(history) return optimizer_result