# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""A wrapper around the local optimization routines implemented in Scipy."""
from typing import Dict, Optional
import numpy
import scipy.optimize
from openfermioncirq.optimization import (BlackBox,
OptimizationResult,
OptimizationAlgorithm)
[docs]class ScipyOptimizationAlgorithm(OptimizationAlgorithm):
"""An optimization algorithm from the scipy.optimize module."""
[docs] def __init__(self,
options: Optional[Dict]=None,
kwargs: Optional[Dict]=None,
uses_bounds: bool=True) -> None:
"""
Args:
options: The `options` dictionary passed to scipy.optimize.minimize.
kwargs: Other keyword arguments passed to scipy.optimize.minimize.
This should NOT include the `bounds` or `options` keyword
arguments.
uses_bounds: Whether the algorithm uses bounds on the input
variables. Set this to False to prevent scipy.optimize.minimize
from raising a warning if the chosen method does not use bounds.
"""
self.kwargs = kwargs or {}
self.uses_bounds = uses_bounds
super().__init__(options)
def optimize(self,
black_box: BlackBox,
initial_guess: Optional[numpy.ndarray]=None,
initial_guess_array: Optional[numpy.ndarray]=None
) -> OptimizationResult:
if initial_guess is None:
raise ValueError('The chosen optimization algorithm requires an '
'initial guess.')
bounds = black_box.bounds if self.uses_bounds else None
result = scipy.optimize.minimize(black_box.evaluate,
initial_guess,
bounds=bounds,
options=self.options,
**self.kwargs)
return OptimizationResult(optimal_value=result.fun,
optimal_parameters=result.x,
num_evaluations=result.nfev,
status=result.status,
message=result.message)
@property
def name(self) -> str:
return self.kwargs.get('method', 'ScipyOptimizationAlgorithm')
COBYLA = ScipyOptimizationAlgorithm(
kwargs={'method': 'COBYLA'},
uses_bounds=False)
L_BFGS_B = ScipyOptimizationAlgorithm(
kwargs={'method': 'L-BFGS-B'})
NELDER_MEAD = ScipyOptimizationAlgorithm(
kwargs={'method': 'Nelder-Mead'},
uses_bounds=False)
SLSQP = ScipyOptimizationAlgorithm(
kwargs={'method': 'SLSQP'})