def _nlopt_callback(available):
if not available:
return
import nlopt # pylint: disable=import-error
_logger.info(
"NLopt version: %s.%s.%s",
nlopt.version_major(),
nlopt.version_minor(),
nlopt.version_bugfix(),
)
# -*- coding: utf-8 -*-
# Copyright 2018 IBM.
#
# 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
# limitations under the License.
# =============================================================================
import nlopt
import logging
logger = logging.getLogger(__name__)
logger.debug('NLopt version: {}.{}.{}'.format(nlopt.version_major(),
nlopt.version_minor(),
nlopt.version_bugfix()))
""" Minimize using objective function """
from typing import List, Optional, Tuple, Callable
from enum import Enum
from abc import abstractmethod
import logging
import numpy as np
from qiskit.aqua import MissingOptionalLibraryError
from ..optimizer import Optimizer, OptimizerSupportLevel
logger = logging.getLogger(__name__)
try:
import nlopt
logger.info('NLopt version: %s.%s.%s', nlopt.version_major(),
nlopt.version_minor(), nlopt.version_bugfix())
_HAS_NLOPT = True
except ImportError:
_HAS_NLOPT = False
class NLoptOptimizerType(Enum):
""" NLopt Valid Optimizer """
GN_CRS2_LM = 1
GN_DIRECT_L_RAND = 2
GN_DIRECT_L = 3
GN_ESCH = 4
GN_ISRES = 5
class NLoptOptimizer(Optimizer):