Source code for gemseo.algos.opt.lib_pdfo

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry,
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                           documentation
#        :author: Jean-Christophe Giret
"""PDFO optimization library wrapper, see `PDFO website <>`_."""
from __future__ import division

import logging

from numpy import inf, isfinite, real
from pdfo import pdfo

from gemseo.algos.opt.opt_lib import OptimizationLibrary
from gemseo.utils.py23_compat import PY2

LOGGER = logging.getLogger(__name__)

[docs]class PDFOOpt(OptimizationLibrary): """PDFO optimization library interface. See OptimizationLibrary. """ LIB_COMPUTE_GRAD = False OPTIONS_MAP = { OptimizationLibrary.MAX_ITER: "max_iter", } def __init__(self): """Constructor. Generate the library dict, contains the list of algorithms with their characteristics: - does it require gradient - does it handle equality constraints - does it handle inequality constraints """ super(PDFOOpt, self).__init__() doc = "" self.lib_dict = { "PDFO_COBYLA": { self.INTERNAL_NAME: "cobyla", self.REQUIRE_GRAD: False, self.POSITIVE_CONSTRAINTS: True, self.HANDLE_EQ_CONS: True, self.HANDLE_INEQ_CONS: True, self.DESCRIPTION: "Constrained Optimization" "By Linear Approximations ", self.WEBSITE: doc, }, "PDFO_BOBYQA": { self.INTERNAL_NAME: "bobyqa", self.REQUIRE_GRAD: False, self.HANDLE_EQ_CONS: False, self.HANDLE_INEQ_CONS: False, self.DESCRIPTION: "Bound Optimization By " "Quadratic Approximation", self.WEBSITE: doc, }, "PDFO_NEWUOA": { self.INTERNAL_NAME: "newuoa", self.REQUIRE_GRAD: False, self.HANDLE_EQ_CONS: False, self.HANDLE_INEQ_CONS: False, self.DESCRIPTION: "NEWUOA", self.WEBSITE: doc, }, } = "PDFO" def _get_options( self, ftol_rel=1e-12, ftol_abs=1e-12, xtol_rel=1e-12, xtol_abs=1e-12, max_time=0, rhobeg=0.5, rhoend=1e-6, max_iter=500, ftarget=-inf, scale=False, quiet=True, classical=False, debug=False, chkfunval=False, ensure_bounds=True, normalize_design_space=True, **kwargs ): r"""Sets the options default values To get the best and up to date information about algorithms options, go to pdfo documentation on the `PDFO website <>`_. :param ftol_rel: stop criteria, relative tolerance on the objective function, if abs(f(xk)-f(xk+1))/abs(f(xk))<= ftol_rel: stop (Default value = 1e-9) :type ftol_rel: float :param ftol_abs: stop criteria, absolute tolerance on the objective function, if abs(f(xk)-f(xk+1))<= ftol_rel: stop (Default value = 1e-9) :type ftol_abs: float :param xtol_rel: stop criteria, relative tolerance on the design variables, if norm(xk-xk+1)/norm(xk)<= xtol_rel: stop (Default value = 1e-9) :type xtol_rel: float :param xtol_abs: stop criteria, absolute tolerance on the design variables, if norm(xk-xk+1)<= xtol_abs: stop (Default value = 1e-9) :type xtol_abs: float :param max_time: maximum runtime in seconds, disabled if 0 (Default value = 0) :type max_time: float :param rhobeg: Initial value of the trust region radius. :type max_iter: float :param rhoend: Final value of the trust region radius. Indicate the accuracy required in the final values of the variables :type rhoend: float :param maxfev: Upper bound of the number of calls of the objective function `fun`. :type maxfev: int :param ftarget: Target value of the objective function. If a feasible iterate achieves an objective function value lower or equal to `options['ftarget']`, the algorithm stops immediately. :type ftarget: float :param scale: Flag indicating whether to scale the problem according to the bound constraints. :type scale: bool :param quiet: Flag of quietness of the interface. If it is set to True, the output message will not be printed. :type quiet: bool :param classical: Flag indicating whether to call the classical Powell code or not. :type classical: bool :param debug: Debugging flag. :type debug: bool :param chkfunval: Flag used when debugging. If both `options['debug']` and `options['chkfunval']` are True, an extra function/constraint evaluation would be performed to check whether the returned values of objective function and constraint match the returned x. :type chkfunval: bool :param ensure_bounds: :type ensure_bounds: bool :param normalize_design_space: If True, normalize the design space :type normalize_design_space: bool """ nds = normalize_design_space popts = self._process_options( ftol_rel=ftol_rel, ftol_abs=ftol_abs, xtol_rel=xtol_rel, xtol_abs=xtol_abs, max_time=max_time, rhobeg=rhobeg, rhoend=rhoend, max_iter=max_iter, ftarget=ftarget, scale=scale, quiet=quiet, classical=classical, debug=debug, chkfunval=chkfunval, ensure_bounds=ensure_bounds, normalize_design_space=nds, **kwargs ) return popts def _run(self, **options): """Runs the algorithm, to be overloaded by subclasses. :param options: the options dict for the algorithm """ # remove normalization from options for algo normalize_ds = options.pop(self.NORMALIZE_DESIGN_SPACE_OPTION, True) # Get the normalized bounds: x_0, l_b, u_b = self.get_x0_and_bounds_vects(normalize_ds) # Ensure bounds ensure_bounds = options["ensure_bounds"] # Replace infinite values with None: l_b = [val if isfinite(val) else None for val in l_b] u_b = [val if isfinite(val) else None for val in u_b] bounds = list(zip(l_b, u_b)) def real_part_fun(x_vect): """Wraps the function and returns the real part.""" return real(self.problem.objective.func(x_vect)) if ensure_bounds: fun = self.ensure_bounds(real_part_fun, normalize_ds) else: fun = real_part_fun constraints = self.get_right_sign_constraints() cstr_scipy = [] for cstr in constraints: if PY2: f_type = cstr.f_type.encode("ascii") else: f_type = cstr.f_type if ensure_bounds: c_scipy = { "type": f_type, "fun": self.ensure_bounds(cstr.func, normalize_ds), } else: c_scipy = {"type": f_type, "fun": cstr.func} cstr_scipy.append(c_scipy) # |g| is in charge of ensuring max iterations, since it may # have a different definition of iterations, such as for SLSQP # for instance which counts duplicate calls to x as a new iteration max_iter = options[self.MAX_ITER] options["maxfev"] = int(max_iter * 1.2) opt_result = pdfo( fun=fun, x0=x_0, method=self.internal_algo_name, bounds=bounds, constraints=cstr_scipy, options=options, ) return self.get_optimum_from_database(opt_result.message, opt_result.status)