# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 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.
"""Constrained Optimization By Linear Approximation optimizer."""
from typing import Optional
import logging
from scipy.optimize import minimize
from .optimizer import Optimizer
logger = logging.getLogger(__name__)
[docs]class COBYLA(Optimizer):
"""
Constrained Optimization By Linear Approximation optimizer.
COBYLA is a numerical optimization method for constrained problems
where the derivative of the objective function is not known.
Uses scipy.optimize.minimize COBYLA.
For further detail, please refer to
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
"""
_OPTIONS = ['maxiter', 'disp', 'rhobeg']
# pylint: disable=unused-argument
def __init__(self,
maxiter: int = 1000,
disp: bool = False,
rhobeg: float = 1.0,
tol: Optional[float] = None) -> None:
"""
Args:
maxiter: Maximum number of function evaluations.
disp: Set to True to print convergence messages.
rhobeg: Reasonable initial changes to the variables.
tol: Final accuracy in the optimization (not precisely guaranteed).
This is a lower bound on the size of the trust region.
"""
super().__init__()
for k, v in locals().items():
if k in self._OPTIONS:
self._options[k] = v
self._tol = tol
[docs] def get_support_level(self):
""" Return support level dictionary """
return {
'gradient': Optimizer.SupportLevel.ignored,
'bounds': Optimizer.SupportLevel.ignored,
'initial_point': Optimizer.SupportLevel.required
}
[docs] def optimize(self, num_vars, objective_function, gradient_function=None,
variable_bounds=None, initial_point=None):
super().optimize(num_vars, objective_function, gradient_function,
variable_bounds, initial_point)
res = minimize(objective_function, initial_point, tol=self._tol,
method="COBYLA", options=self._options)
return res.x, res.fun, res.nfev