# Quellcode für qiskit.optimization.algorithms.optimization_algorithm

# This code is part of Qiskit.
#
# (C) Copyright IBM 2020, 2021.
#
# obtain a copy of this license in the LICENSE.txt file in the root directory
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
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"""An abstract class for optimization algorithms in Qiskit's optimization module."""

from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum
from typing import List, Union, Any, Optional, Dict, Type, cast
from warnings import warn

import numpy as np

from .. import QiskitOptimizationError
[Doku]class OptimizationResult: """A base class for optimization results. The optimization algorithms return an object of the type OptimizationResult with the information about the solution obtained. OptimizationResult allows users to get the value of a variable by specifying an index or a name as follows. Examples: >>> from qiskit.optimization import QuadraticProgram >>> from qiskit.optimization.algorithms import CplexOptimizer >>> problem = QuadraticProgram() >>> _ = problem.binary_var('x1') >>> _ = problem.binary_var('x2') >>> _ = problem.binary_var('x3') >>> problem.minimize(linear={'x1': 1, 'x2': -2, 'x3': 3}) >>> print([var.name for var in problem.variables]) ['x1', 'x2', 'x3'] >>> optimizer = CplexOptimizer() >>> result = optimizer.solve(problem) >>> print(result.variable_names) ['x1', 'x2', 'x3'] >>> print(result.x) [0. 1. 0.] >>> print(result[1]) 1.0 >>> print(result['x1']) 0.0 >>> print(result.fval) -2.0 >>> print(result.variables_dict) {'x1': 0.0, 'x2': 1.0, 'x3': 0.0} Note: The order of variables should be equal to that of the problem solved by optimization algorithms. Optimization algorithms and converters of QuadraticProgram should maintain the order when generating a new OptimizationResult object. """
[Doku] def __init__(self, x: Optional[Union[List[float], np.ndarray]], fval: float, variables: List[Variable], status: OptimizationResultStatus, raw_results: Optional[Any] = None, samples: Optional[List[SolutionSample]] = None) -> None: """ Args: x: the optimal value found in the optimization, or possibly None in case of FAILURE. fval: the optimal function value. variables: the list of variables of the optimization problem. raw_results: the original results object from the optimization algorithm. status: the termination status of the optimization algorithm. samples: the solution samples. Raises: QiskitOptimizationError: if sizes of x and variables do not match. """ self._variables = variables self._variable_names = [var.name for var in self._variables] if x is None: # if no state is given, it is set to None self._x = None # pylint: disable=invalid-name self._variables_dict = None else: if len(x) != len(variables): raise QiskitOptimizationError( 'Inconsistent size of optimal value and variables. x: size {} {}, ' 'variables: size {} {}'.format(len(x), x, len(variables), [v.name for v in variables])) self._x = np.asarray(x) self._variables_dict = dict(zip(self._variable_names, self._x)) self._fval = fval self._raw_results = raw_results self._status = status if samples: sum_prob = np.sum([e.probability for e in samples]) if not np.isclose(sum_prob, 1.0): warn('The sum of probability of samples is not close to 1: {}'.format(sum_prob)) self._samples = samples else: self._samples = [ SolutionSample(x=cast(np.ndarray, x), fval=fval, status=status, probability=1.0)]
def __repr__(self) -> str: return 'optimal function value: {}\n' \ 'optimal value: {}\n' \ 'status: {}'.format(self._fval, self._x, self._status.name) def __getitem__(self, key: Union[int, str]) -> float: """Returns the value of the variable whose index or name is equal to key. The key can be an integer or a string. If the key is an integer, this methods returns the value of the variable whose index is equal to key. If the key is a string, this methods return the value of the variable whose name is equal to key. Args: key: an integer or a string. Returns: The value of a variable whose index or name is equal to key. Raises: IndexError: if key is an integer and is out of range of the variables. KeyError: if key is a string and none of the variables has key as name. TypeError: if key is neither an integer nor a string. """ if isinstance(key, int): return self._x[key] if isinstance(key, str): return self._variables_dict[key] raise TypeError( "Integer or string key required," "instead {}({}) provided.".format(type(key), key)) @property def x(self) -> Optional[np.ndarray]: """Returns the optimal value found in the optimization or None in case of FAILURE. Returns: The optimal value found in the optimization. """ return self._x @property def fval(self) -> float: """Returns the optimal function value. Returns: The function value corresponding to the optimal value found in the optimization. """ return self._fval @property def raw_results(self) -> Any: """Return the original results object from the optimization algorithm. Currently a dump for any leftovers. Returns: Additional result information of the optimization algorithm. """ return self._raw_results @property def status(self) -> OptimizationResultStatus: """Returns the termination status of the optimization algorithm. Returns: The termination status of the algorithm. """ return self._status @property def variables(self) -> List[Variable]: """Returns the list of variables of the optimization problem. Returns: The list of variables. """ return self._variables @property def variables_dict(self) -> Dict[str, float]: """Returns the optimal value as a dictionary of the variable name and corresponding value. Returns: The optimal value as a dictionary of the variable name and corresponding value. """ return self._variables_dict @property def variable_names(self) -> List[str]: """Returns the list of variable names of the optimization problem. Returns: The list of variable names of the optimization problem. """ return self._variable_names @property def samples(self) -> List[SolutionSample]: """Returns the list of solution samples Returns: The list of solution samples. """ return self._samples
def _verify_compatibility(self, problem: QuadraticProgram) -> None: """Verifies that the problem is suitable for this optimizer. If the problem is not compatible then an exception is raised. This method is for convenience for concrete optimizers and is not intended to be used by end user. Args: problem: Problem to verify. Returns: None Raises: QiskitOptimizationError: If the problem is incompatible with the optimizer. """ # check compatibility and raise exception if incompatible msg = self.get_compatibility_msg(problem) if msg: raise QiskitOptimizationError('Incompatible problem: {}'.format(msg)) @staticmethod def _get_feasibility_status(problem: QuadraticProgram, x: Union[List[float], np.ndarray]) -> OptimizationResultStatus: """Returns whether the input result is feasible or not for the given problem. Args: problem: Problem to verify. x: the input result list. Returns: The status of the result. """ is_feasible = problem.is_feasible(x) return OptimizationResultStatus.SUCCESS if is_feasible \ else OptimizationResultStatus.INFEASIBLE @staticmethod def _prepare_converters(converters: Optional[Union[QuadraticProgramConverter, List[QuadraticProgramConverter]]], penalty: Optional[float] = None) -> List[QuadraticProgramConverter]: """Prepare a list of converters from the input. Args: converters: The converters to use for converting a problem into a different form. By default, when None is specified, an internally created instance of :class:~qiskit.optimization.converters.QuadraticProgramToQubo will be used. penalty: The penalty factor used in the default :class:~qiskit.optimization.converters.QuadraticProgramToQubo converter Returns: The list of converters. Raises: TypeError: When the converters include those that are not :class:~qiskit.optimization.converters.QuadraticProgramConverter type. """ if converters is None: return [QuadraticProgramToQubo(penalty=penalty)] elif isinstance(converters, QuadraticProgramConverter): return [converters] elif isinstance(converters, list) and \ all(isinstance(converter, QuadraticProgramConverter) for converter in converters): return converters else: raise TypeError('converters must all be of the QuadraticProgramConverter type') @staticmethod def _convert(problem: QuadraticProgram, converters: Union[QuadraticProgramConverter, List[QuadraticProgramConverter]]) -> QuadraticProgram: """Convert the problem with the converters Args: problem: The problem to be solved converters: The converters to use for converting a problem into a different form. Returns: The problem converted by the converters. """ problem_ = problem if not isinstance(converters, list): converters = [converters] for converter in converters: problem_ = converter.convert(problem_) return problem_ @classmethod def _interpret(cls, x: np.ndarray, problem: QuadraticProgram, converters: Optional[Union[QuadraticProgramConverter, List[QuadraticProgramConverter]]] = None, result_class: Type[OptimizationResult] = OptimizationResult, **kwargs) -> OptimizationResult: """Convert back the result of the converted problem to the result of the original problem. Args: x: The result of the converted problem. converters: The converters to use for converting back the result of the problem to the result of the original problem. problem: The original problem for which x` is interpreted. result_class: The class of the result object. kwargs: parameters of the constructor of result_class Returns: The result of the original problem. Raises: QiskitOptimizationError: if result_class is not a sub-class of OptimizationResult. TypeError: if converters are not QuadraticProgramConverter or a list of QuadraticProgramConverter. """ if not issubclass(result_class, OptimizationResult): raise QiskitOptimizationError( 'Invalid result class, not derived from OptimizationResult: ' '{}'.format(result_class)) if converters is None: converters = [] if not isinstance(converters, list): converters = [converters] if not all(isinstance(conv, QuadraticProgramConverter) for conv in converters): raise TypeError('Invalid object of converters: {}'.format(converters)) for converter in converters[::-1]: x = converter.interpret(x) return result_class(x=x, fval=problem.objective.evaluate(x), variables=problem.variables, status=cls._get_feasibility_status(problem, x), **kwargs)