Source code for qiskit_optimization.algorithms.cobyla_optimizer

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# (C) Copyright IBM 2020, 2021.
# 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.
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"""The COBYLA optimizer wrapped to be used within Qiskit's optimization module."""

from typing import Optional, cast, List, Tuple, Any

import numpy as np
from scipy.optimize import fmin_cobyla

from .multistart_optimizer import MultiStartOptimizer
from .optimization_algorithm import OptimizationResult
from ..exceptions import QiskitOptimizationError
from ..infinity import INFINITY
from ..problems.constraint import Constraint
from ..problems.quadratic_program import QuadraticProgram
from ..converters import MaximizeToMinimize

[docs]class CobylaOptimizer(MultiStartOptimizer): """The SciPy COBYLA optimizer wrapped as an Qiskit :class:`OptimizationAlgorithm`. This class provides a wrapper for ``scipy.optimize.fmin_cobyla`` (https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.optimize.fmin_cobyla.html) to be used within the optimization module. The arguments for ``fmin_cobyla`` are passed via the constructor. Examples: >>> from qiskit_optimization.problems import QuadraticProgram >>> from qiskit_optimization.algorithms import CobylaOptimizer >>> problem = QuadraticProgram() >>> # specify problem here >>> optimizer = CobylaOptimizer() >>> result = optimizer.solve(problem) """ def __init__( self, rhobeg: float = 1.0, rhoend: float = 1e-4, maxfun: int = 1000, disp: Optional[int] = None, catol: float = 2e-4, trials: int = 1, clip: float = 100.0, ) -> None: """Initializes the CobylaOptimizer. This initializer takes the algorithmic parameters of COBYLA and stores them for later use of ``fmin_cobyla`` when :meth:`solve` is invoked. This optimizer can be applied to find a (local) optimum for problems consisting of only continuous variables. Args: rhobeg: Reasonable initial changes to the variables. rhoend: Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region. disp: Controls the frequency of output; 0 implies no output. Feasible values are {0, 1, 2, 3}. maxfun: Maximum number of function evaluations. catol: Absolute tolerance for constraint violations. trials: The number of trials for multi-start method. The first trial is solved with the initial guess of zero. If more than one trial is specified then initial guesses are uniformly drawn from ``[lowerbound, upperbound]`` with potential clipping. clip: Clipping parameter for the initial guesses in the multi-start method. If a variable is unbounded then the lower bound and/or upper bound are replaced with the ``-clip`` or ``clip`` values correspondingly for the initial guesses. """ super().__init__(trials, clip) self._rhobeg = rhobeg self._rhoend = rhoend self._maxfun = maxfun self._disp = disp self._catol = catol
[docs] def get_compatibility_msg(self, problem: QuadraticProgram) -> str: """Checks whether a given problem can be solved with this optimizer. Checks whether the given problem is compatible, i.e., whether the problem contains only continuous variables, and otherwise, returns a message explaining the incompatibility. Args: problem: The optimization problem to check compatibility. Returns: Returns a string describing the incompatibility. """ # check whether there are variables of type other than continuous if len(problem.variables) > problem.get_num_continuous_vars(): return "The COBYLA optimizer supports only continuous variables" return ""
[docs] def solve(self, problem: QuadraticProgram) -> OptimizationResult: """Tries to solves the given problem using the optimizer. Runs the optimizer to try to solve the optimization problem. Args: problem: The problem to be solved. Returns: The result of the optimizer applied to the problem. Raises: QiskitOptimizationError: If the problem is incompatible with the optimizer. """ self._verify_compatibility(problem) # we deal with minimization in the optimizer, so turn the problem to minimization max2min = MaximizeToMinimize() original_problem = problem problem = self._convert(problem, max2min) # initialize constraints list constraints = [] # add lower/upper bound constraints for i, variable in enumerate(problem.variables): lowerbound = variable.lowerbound upperbound = variable.upperbound if lowerbound > -INFINITY: def lb_constraint(x, l_b=lowerbound, j=i): return x[j] - l_b constraints += [lb_constraint] if upperbound < INFINITY: def ub_constraint(x, u_b=upperbound, j=i): return u_b - x[j] constraints += [ub_constraint] # pylint: disable=no-member # add linear and quadratic constraints for constraint in cast(List[Constraint], problem.linear_constraints) + cast( List[Constraint], problem.quadratic_constraints ): rhs = constraint.rhs sense = constraint.sense if sense == Constraint.Sense.EQ: constraints += [ lambda x, rhs=rhs, c=constraint: rhs - c.evaluate(x), lambda x, rhs=rhs, c=constraint: c.evaluate(x) - rhs, ] elif sense == Constraint.Sense.LE: constraints += [lambda x, rhs=rhs, c=constraint: rhs - c.evaluate(x)] elif sense == Constraint.Sense.GE: constraints += [lambda x, rhs=rhs, c=constraint: c.evaluate(x) - rhs] else: raise QiskitOptimizationError("Unsupported constraint type!") # actual minimization function to be called by multi_start_solve def _minimize(x_0: np.ndarray) -> Tuple[np.ndarray, Any]: x = fmin_cobyla( problem.objective.evaluate, x_0, constraints, rhobeg=self._rhobeg, rhoend=self._rhoend, maxfun=self._maxfun, disp=self._disp, catol=self._catol, ) return x, None result = self.multi_start_solve(_minimize, problem) # eventually convert back minimization to maximization return self._interpret( x=result.x, problem=original_problem, converters=max2min, raw_results=result.raw_results )