# -*- 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: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Scalable problem - Models
*************************
"""
from __future__ import division, unicode_literals
import logging
from numpy import array, atleast_2d, diag, dot, eye
from numpy import mean as npmean
from numpy import ndarray
from numpy import sum as npsum
from past.utils import old_div
from .variables import (
OBJECTIVE_NAME,
X_SHARED_NAME,
get_constraint_name,
get_coupling_name,
get_u_local_name,
get_x_local_name,
)
LOGGER = logging.getLogger(__name__)
[docs]class TMMainModel(object):
r"""The main discipline from the scalable problem introduced by Tedford
and Martins (2010) takes the local design parameters
:math:`x_1,x_2,\ldots,x_N` and the global design parameters :math:`z`
as inputs, as well as the coupling variables :math:`y_1,y_2,\ldots,y_N`
and returns the objective function value :math:`f(x,y(x,y))` to minimize as
well as the inequality constraints ones
:math:`c_1(y_1),c_2(y_2),\ldots,c_N(y_N)` which are expressed as:
.. math::
f(z,y) = |z|_2^2 + \sum_{i=1}^N |y_i|_2^2
and:
.. math::
c_i(y_i) = 1- C_i^{-T}Iy_i
"""
def __init__(self, c_constraint, default_inputs):
"""Constructor.
:param list(ndarray) c_constraint: constraint coefficients
:param dict(ndarray) default_inputs: default inputs
"""
self.name = "MainModel"
self.default_inputs = default_inputs
self.inputs_sizes = {name: len(val) for name, val in default_inputs.items()}
self.inputs_names = sorted(self.inputs_sizes.keys())
self.outputs_sizes = {
get_constraint_name(index): len(value)
for index, value in enumerate(c_constraint)
}
self.outputs_sizes[OBJECTIVE_NAME] = 1
self.outputs_names = sorted(self.outputs_sizes.keys())
self.coefficients = c_constraint
self.n_submodels = len(c_constraint)
def __call__(self, x_shared=None, coupling=None, jacobian=False):
"""Compute the discipline output or derivatives for new values of shared design
parameters and coupling variables.
:param ndarray x_shared: shared design parameters.
:param dict(ndarray) coupling: list of coupling variables
(one element per sub-discipline).
:param bool jacobian: if True, return the jacobian of the discipline output.
Otherwise, return the discipline output.
"""
if x_shared is None:
x_shared = self.default_inputs[X_SHARED_NAME]
if coupling is None:
names = set(self.inputs_names) - {X_SHARED_NAME}
coupling = {name: self.default_inputs[name] for name in names}
if jacobian:
result = self._compute_jacobian(x_shared, coupling)
else:
result = self._compute_output(x_shared, coupling)
return result
def _compute_output(self, x_shared, coupling):
"""Compute the discipline output for new values of shared design parameters and
coupling variables.
:param ndarray x_shared: shared design parameters.
:param dict(ndarray) coupling: list of coupling variables
(one element per sub-discipline).
"""
obj = npmean(x_shared ** 2)
obj += npmean(
[
npmean(coupling[get_coupling_name(index)] ** 2)
for index in range(self.n_submodels)
]
)
output = {OBJECTIVE_NAME: array([obj])}
for index in range(self.n_submodels):
constraint = get_constraint_name(index)
coupling_name = get_coupling_name(index)
tmp = old_div(coupling[coupling_name], self.coefficients[index])
output[constraint] = 1 - tmp
return output
def _compute_jacobian(self, x_shared, coupling):
"""Computes the discipline jacobian.
:param ndarray x_shared: shared design parameters.
:param dict(ndarray) coupling: list of coupling variables
(one element per sub-discipline).
"""
tmp = old_div(2 * x_shared, len(x_shared))
jacobian = {OBJECTIVE_NAME: {}}
jacobian[OBJECTIVE_NAME][X_SHARED_NAME] = atleast_2d(tmp)
for index in range(self.n_submodels):
constraint = get_constraint_name(index)
coupling_name = get_coupling_name(index)
jacobian[constraint] = {}
tmp = old_div(2 * coupling[coupling_name], len(coupling[coupling_name]))
tmp /= self.n_submodels
jacobian[OBJECTIVE_NAME][coupling_name] = atleast_2d(tmp)
tmp = diag(old_div(-1.0, self.coefficients[index]))
jacobian[constraint][coupling_name] = atleast_2d(tmp)
return jacobian
[docs]class TMSubModel(object):
r"""A sub-discipline from the scalable problem introduced by
Tedford and Martins (2010) takes local design parameters :math:`x_i`
and shared design parameters :math:`z` in input as well as coupling
variables :math:`\left(y_i\right)_{1\leq j \leq N\atop j\neq i}`
from :math:`N-1` elementary disciplines,
and returns the coupling variables:
.. math::
y_i =\frac{\tilde{y}_i+C_{z,i}.1+C_{x_i}.1}{\sum_{j=1 \atop j
\neq i}^NC_{y_j,i}.1+C_{z,i}.1+C_{x_i}.1} \in [0,1]^{n_{y_i}}
where:
.. math::
\tilde{y}_i = - C_{z,i}.z - C_{x_i}.x_i +
\sum_{j=1 \atop j \neq i}^N C_{y_j,i}.y_j
"""
def __init__(self, index, c_shared, c_local, c_coupling, default_inputs):
"""Constructor.
:param int index: discipline index for naming.
:param ndarray c_shared: weights for the shared design parameters.
:param ndarray c_local: weights for the local design parameters.
:param dict(ndarray) c_coupling: weights for the coupling parameters.
:param dict(ndarray) default_inputs: default inputs
"""
self.name = "SubModel_{}".format(index)
self.index = index
self.c_shared = c_shared
self.c_local = c_local
self.c_coupling = c_coupling
self.default_inputs = default_inputs
self._check_consistency()
self.inputs_sizes = {name: len(val) for name, val in default_inputs.items()}
self.inputs_names = sorted(self.inputs_sizes.keys())
output = get_coupling_name(index)
self.outputs_sizes = {output: len(c_local)}
self.outputs_names = sorted(self.outputs_sizes.keys())
def _check_consistency(self):
"""Check consistency of model and default inputs."""
assert isinstance(self.c_shared, ndarray)
assert len(self.c_shared.shape) == 2
assert self.c_shared.shape[1] == len(self.default_inputs[X_SHARED_NAME])
nout = self.c_shared.shape[0]
assert isinstance(self.c_local, ndarray)
assert len(self.c_local.shape) == 2
assert self.c_local.shape[0] == nout
xlocal = get_x_local_name(self.index)
assert self.c_local.shape[1] == len(self.default_inputs[xlocal])
assert isinstance(self.c_coupling, dict)
for key, val in self.c_coupling.items():
assert isinstance(val, ndarray)
assert len(val.shape) == 2
assert val.shape[0] == nout
assert val.shape[1] == len(self.default_inputs[key])
def __call__(
self, x_shared=None, x_local=None, coupling=None, noise=None, jacobian=False
):
"""Compute the discipline output or derivatives for new values of shared design
parameters and coupling variables.
:param ndarray x_shared: shared design parameters.
:param ndarray x_local: local design parameters.
:param dict(ndarray) coupling: list of coupling variables
(one element per sub-discipline).
:param ndarray noise: random noise applied to the vectorial output.
:param bool jacobian: if True, return the jacobian of the discipline output.
Otherwise, return the discipline output.
"""
if x_shared is None:
x_shared = self.default_inputs[X_SHARED_NAME]
if x_local is None:
x_local = self.default_inputs[get_x_local_name(self.index)]
if coupling is None:
coupling = {cpl: self.default_inputs[cpl] for cpl in self.c_coupling}
if jacobian:
result = self._compute_jacobian()
else:
result = self._compute_output(x_shared, x_local, coupling, noise)
return result
def _compute_output(self, x_shared, x_local, coupling, noise):
"""Compute the discipline output for new values of shared design parameters and
coupling variables.
:param ndarray x_shared: shared design parameters.
:param ndarray x_local: local design parameters.
:param dict(ndarray) coupling: list of coupling variables
(one element per sub-discipline).
:param ndarray noise: random noise applied to the vectorial output.
"""
output = -dot(self.c_shared, x_shared.reshape((-1, 1)))
output -= dot(self.c_local, x_local.reshape((-1, 1)))
cpl_sum = 0
for name, coeff in self.c_coupling.items():
output += dot(coeff, coupling[name].reshape((-1, 1)))
cpl_sum += npsum(coeff, 1)
output += npsum(self.c_shared, 1).reshape((-1, 1))
output += npsum(self.c_local, 1).reshape((-1, 1))
norm = cpl_sum.reshape((-1, 1))
norm += npsum(self.c_shared, 1).reshape((-1, 1))
norm += npsum(self.c_local, 1).reshape((-1, 1))
output /= norm
output = output.flatten()
if noise is not None:
output += noise
output = {get_coupling_name(self.index): output}
return output
def _compute_jacobian(self):
"""Computes the discipline jacobian."""
coupling_name = get_coupling_name(self.index)
x_local_name = get_x_local_name(self.index)
u_local_name = get_u_local_name(self.index)
cpl_sum = 0
for coeff in self.c_coupling.values():
cpl_sum += npsum(coeff, 1)
norm = cpl_sum.reshape((-1, 1))
norm += npsum(self.c_shared, 1).reshape((-1, 1))
norm += npsum(self.c_local, 1).reshape((-1, 1))
der = old_div(-self.c_local, norm)
jacobian = {coupling_name: {}}
jacobian[coupling_name][x_local_name] = der
jacobian[coupling_name][u_local_name] = eye(der.shape[0])
der = old_div(-self.c_shared, norm)
jacobian[coupling_name][X_SHARED_NAME] = der
for cpl_name, cpl_value in self.c_coupling.items():
jacobian[coupling_name][cpl_name] = old_div(cpl_value, norm)
return jacobian
