Source code for gemseo.algos.opt.lib_scipy

# -*- 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: Damien Guenot
#         Francois Gallard : refactoring for v1, May 2016
"""scipy.optimize optimization library wrapper."""
from __future__ import division, unicode_literals

import logging
from typing import Any, Dict, Optional, Union

from numpy import isfinite, ndarray, real
from scipy import optimize

from gemseo.algos.opt.opt_lib import OptimizationLibrary
from gemseo.algos.opt_result import OptimizationResult

LOGGER = logging.getLogger(__name__)

[docs]class ScipyOpt(OptimizationLibrary): """Scipy optimization library interface. See OptimizationLibrary. """ LIB_COMPUTE_GRAD = True OPTIONS_MAP = { # Available only in the doc ! OptimizationLibrary.LS_STEP_NB_MAX: "maxls", OptimizationLibrary.LS_STEP_SIZE_MAX: "stepmx", OptimizationLibrary.MAX_FUN_EVAL: "maxfun", OptimizationLibrary.PG_TOL: "gtol", } 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(ScipyOpt, self).__init__() doc = "" self.lib_dict = { "SLSQP": { self.INTERNAL_NAME: "SLSQP", self.REQUIRE_GRAD: True, self.POSITIVE_CONSTRAINTS: True, self.HANDLE_EQ_CONS: True, self.HANDLE_INEQ_CONS: True, self.DESCRIPTION: "Sequential Least-Squares Quadratic " "Programming (SLSQP) implemented in " "the SciPy library", self.WEBSITE: doc + "optimize.minimize-slsqp.html", }, "L-BFGS-B": { self.INTERNAL_NAME: "L-BFGS-B", self.REQUIRE_GRAD: True, self.HANDLE_EQ_CONS: False, self.HANDLE_INEQ_CONS: False, self.DESCRIPTION: "Limited-memory BFGS algorithm " "implemented in SciPy library", self.WEBSITE: doc + "generated/scipy.optimize.fmin_l_bfgs_b.html", }, "TNC": { self.INTERNAL_NAME: "TNC", self.REQUIRE_GRAD: True, self.HANDLE_EQ_CONS: False, self.HANDLE_INEQ_CONS: False, self.DESCRIPTION: "Truncated Newton (TNC) algorithm " "implemented in SciPy library", self.WEBSITE: doc + "optimize.minimize-tnc.html", }, } def _get_options( self, max_iter=999, # type: int ftol_rel=1e-9, # type: float ftol_abs=1e-9, # type: float xtol_rel=1e-9, # type: float xtol_abs=1e-9, # type: float max_ls_step_size=0.0, # type: float max_ls_step_nb=20, # type: int max_fun_eval=999, # type: int max_time=0, # type: float pg_tol=1e-5, # type: float disp=0, # type: int maxCGit=-1, # type: int # noqa: N803 eta=-1.0, # type: float factr=1e7, # type: float maxcor=20, # type: int normalize_design_space=True, # type: int eq_tolerance=1e-2, # type: float ineq_tolerance=1e-4, # type: float stepmx=0.0, # type: float minfev=0.0, # type: float scale=None, # type: Optional[float] rescale=-1, # type: float offset=None, # type: Optional[float] **kwargs # type: Any ): # type: (...) -> Dict[str, Any] r"""Set the options default values. To get the best and up to date information about algorithms options, go to scipy.optimize documentation: Args: max_iter: The maximum number of iterations, i.e. unique calls to f(x). ftol_rel: A stop criteria, the relative tolerance on the objective function. If abs(f(xk)-f(xk+1))/abs(f(xk))<= ftol_rel: stop. ftol_abs: A stop criteria, the absolute tolerance on the objective function. If abs(f(xk)-f(xk+1))<= ftol_rel: stop. xtol_rel: A stop criteria, the relative tolerance on the design variables. If norm(xk-xk+1)/norm(xk)<= xtol_rel: stop. xtol_abs: A stop criteria, absolute tolerance on the design variables. If norm(xk-xk+1)<= xtol_abs: stop. max_ls_step_size: The maximum step for the line search. max_ls_step_nb: The maximum number of line search steps per iteration. max_fun_eval: The internal stop criteria on the number of algorithm outer iterations. max_time: The maximum runtime in seconds, disabled if 0. pg_tol: A stop criteria on the projected gradient norm. disp: The display information flag. maxCGit: The maximum Conjugate Gradient internal solver iterations. eta: The severity of the line search, specific to the TNC algorithm. factr: A stop criteria on the projected gradient norm, stop if max_i (grad_i)<eps_mach \* factr, where eps_mach is the machine precision. maxcor: The maximum BFGS updates. normalize_design_space: If True, scales variables to [0, 1]. eq_tolerance: The equality tolerance. ineq_tolerance: The inequality tolerance. stepmx: The maximum step for the line search. minfev: The minimum function value estimate. scale: The scaling factor to apply to each variable. If None, the factors are up-low for interval bounded variables and 1+|x| for the others. rescale: The scaling factor (in log10) used to trigger f value rescaling. offset: Value to subtract from each variable. If None, the offsets are (up+low)/2 for interval bounded variables and x for the others. **kwargs: The other algorithm options. """ nds = normalize_design_space popts = self._process_options( max_iter=max_iter, ftol_rel=ftol_rel, ftol_abs=ftol_abs, xtol_rel=xtol_rel, xtol_abs=xtol_abs, max_time=max_time, max_ls_step_size=max_ls_step_size, max_ls_step_nb=max_ls_step_nb, max_fun_eval=max_fun_eval, pg_tol=pg_tol, disp=disp, maxCGit=maxCGit, # noqa: N803 eta=eta, factr=factr, maxcor=maxcor, normalize_design_space=nds, ineq_tolerance=ineq_tolerance, eq_tolerance=eq_tolerance, stepmx=stepmx, minfev=minfev, scale=scale, rescale=rescale, offset=offset, **kwargs ) return popts def _run( self, **options # type: Any ): # type: (...) -> OptimizationResult """Run the algorithm, to be overloaded by subclasses. Args: **options: The options for the algorithm. Returns: The optimization result. """ # 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) # 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, # type: ndarray ): # type: (...) -> Union[int, float] """Wrap the function and return the real part. Args: x: The values to be given to the function. Returns: The real part of the evaluation of the objective function. """ return real(self.problem.objective.func(x)) fun = real_part_fun constraints = self.get_right_sign_constraints() cstr_scipy = [] for cstr in constraints: c_scipy = {"type": cstr.f_type, "fun": cstr.func, "jac": cstr.jac} cstr_scipy.append(c_scipy) jac = self.problem.objective.jac # |g| is in charge of ensuring max iterations, and # xtol, ftol, 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 options["maxiter"] = 10000000 # Deactivate scipy stop criteria to use |g|' ones options["ftol"] = 0.0 options["xtol"] = 0.0 options.pop(self.F_TOL_ABS) options.pop(self.X_TOL_ABS) options.pop(self.F_TOL_REL) options.pop(self.X_TOL_REL) options.pop(self.MAX_TIME) options.pop(self.MAX_ITER) if self.algo_name != "TNC": options.pop("xtol") opt_result = optimize.minimize( fun=fun, x0=x_0, method=self.internal_algo_name, jac=jac, bounds=bounds, constraints=cstr_scipy, tol=None, options=options, ) return self.get_optimum_from_database(opt_result.message, opt_result.status)