# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2019, 2023.
#
# 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_algorithms.utils import optionals as _optionals
from .optimizer import Optimizer, OptimizerSupportLevel, OptimizerResult, POINT
[docs]@_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
[docs] 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}
[docs] 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