# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# 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
# OTHER AUTHORS - MACROSCOPIC CHANGES
# Francois Gallard : refactoring for v1, May 2016
"""
scipy.optimize optimization library wrapper
"""
from __future__ import absolute_import, division, print_function, unicode_literals
from builtins import super, zip
from future import standard_library
from numpy import isfinite, real
from scipy import optimize
from gemseo.algos.opt.opt_lib import OptimizationLibrary
standard_library.install_aliases()
from gemseo import LOGGER
[docs]class ScipyOpt(OptimizationLibrary):
"""Scipy optimization library interface.
See OptimizationLibrary.
"""
LIB_COMPUTE_GRAD = True
OPTIONS_MAP = {
OptimizationLibrary.MAX_ITER: "maxiter",
OptimizationLibrary.F_TOL_REL: "ftol",
OptimizationLibrary.X_TOL_REL: "xtol",
# 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 = "https://docs.scipy.org/doc/scipy/reference/"
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,
ftol_rel=1e-9, # pylint: disable=W0221
ftol_abs=1e-9,
xtol_rel=1e-9,
xtol_abs=1e-9,
max_ls_step_size=0.0,
max_ls_step_nb=20,
max_fun_eval=999,
max_time=-1,
pg_tol=1e-5,
disp=0,
maxCGit=-1,
eta=-1.0,
factr=1e7,
maxcor=20,
normalize_design_space=True,
eq_tolerance=1e-2,
ineq_tolerance=1e-4,
stepmx=0.0,
minfev=0.0,
scale=None,
rescale=None,
offset=None,
**kwargs
):
r"""Sets the options default values
To get the best and up to date information about algorithms options,
go to scipy.optimize documentation:
https://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html
:param max_iter: maximum number of iterations, ie unique calls to f(x)
:type max_iter: int
: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_ls_step_size: Maximum step for the line search
(Default value = 0.)
:type max_ls_step_size: float
:param max_ls_step_nb: Maximum number of line search steps
per iteration. (Default value = 20)
:type max_ls_step_nb: int
:param max_fun_eval: internal stop criteria on the
number of algorithm outer iterations (Default value = 999)
:type max_ls_step_size: int
:param max_time: maximum runtime (Default value = -1)
:type max_time: float
:param pg_tol: stop criteria on the projected gradient norm
(Default value = 1e-5)
:type pg_tol: float
:param disp: display information, (Default value = 0)
:type disp: int
:param maxCGit: Maximum Conjugate Gradient internal solver
iterations (Default value = -1)
:type maxCGit: int
:param eta: severity of the linesearch, specific to
TNC algorithm (Default value = -1.)
:type eta: int
:param factr: stop criteria on the projected gradient norm,
stop if max_i (grad_i)<eps_mach \* factr, where eps_mach is the
machine precision ( Default value = 1e7)
:type factr: float
:param maxcor: maximum BFGS updates (Default value = 20)
:type maxcor: int
:param normalize_design_space: If True, scales variables in [0, 1]
:type normalize_design_space: bool
:param eq_tolerance: equality tolerance
:type eq_tolerance: float
:param ineq_tolerance: inequality tolerance
:type ineq_tolerance: float
:param stepmx: Maximum step for the line search.
:type stepmx: float
:param minfev: Minimum function value estimate
:type minfev: float
:param scale: Scaling factors to apply to each variable
:type scale: array
:param rescale: Scaling factor (in log10) used to trigger f value
rescaling
:type rescale: float
:param offset: Value to subtract from each variable.
:type offset: float
:param kwargs: other algorithms options
:tupe kwargs: kwargs
"""
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_ls_step_size=max_ls_step_size,
max_ls_step_nb=max_ls_step_nb,
max_fun_eval=max_fun_eval,
max_time=max_time,
pg_tol=pg_tol,
disp=disp,
maxCGit=maxCGit,
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):
"""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)
# 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))
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, 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[self.OPTIONS_MAP[self.MAX_ITER]] = 10000000
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)
