# Source code for gemseo.algos.lagrange_multipliers

# -*- 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
#
# 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 - API and implementation and/or documentation
#       :author: Pierre-Jean Barjhoux
#       :author: Francois Gallard, integration and cleanup
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""
Implementation of the Lagrange multipliers
******************************************
"""
from __future__ import division, unicode_literals

import logging

from numpy import arange, array, atleast_2d, concatenate, where, zeros
from numpy.linalg import lstsq, matrix_rank, norm
from scipy.optimize import linprog

from gemseo.algos.design_space import DesignSpace
from gemseo.algos.opt_problem import OptimizationProblem
from gemseo.third_party.prettytable import PrettyTable

LOGGER = logging.getLogger(__name__)

[docs]class LagrangeMultipliers(object):
r"""
Class that implements the computation of Lagrange Multipliers

Denote :math:x^\ast an optimal solution of the optimization problem
below.

.. math::
\begin{aligned}
& \text{Minimize}    & & f(x) \\
& \text{relative to} & & x \\
& \text{subject to}  & & \left\{\begin{aligned}
& g(x)\le0, \\
& h(x)=0, \\
& \ell\le x\le u.
\end{aligned}\right.
\end{aligned}

If the constraints are qualified at :math:x^\ast then the Lagrange
multipliers of :math:x^\ast are the vectors :math:\lambda_g,
:math:\lambda_h, :math:\lambda_\ell and :math:\lambda_u satisfying

.. math::
\left\{\begin{aligned}
&\frac{\partial f}{\partial x}(x^\ast)
+\lambda_g^\top\frac{\partial g}{\partial x}(x^\ast)
+\lambda_h^\top\frac{\partial h}{\partial x}(x^\ast)
+\sum_j\lambda_{\ell,j}+\sum_j\lambda_{u,j}
=0,\\
&\lambda_{g,i}\ge0\text{ if }g_i(x^\ast)=0,
\text{ otherwise }\lambda_{g,i}=0,\\
&\lambda_{\ell,j}\le0\text{ if }x^\ast_j=\ell_j,
\text{ otherwise }\lambda_{\ell,j}=0,\\
&\lambda_{u,j}\ge0\text{ if }x^\ast_j=u_j,
\text{ otherwise }\lambda_{u,j}=0.
\end{aligned}\right.

"""
LOWER_BOUNDS = "lower_bounds"
UPPER_BOUNDS = "upper_bounds"
INEQUALITY = "inequality"
EQUALITY = "equality"
CSTR_LABELS = [LOWER_BOUNDS, UPPER_BOUNDS, INEQUALITY, EQUALITY]

def __init__(self, opt_problem):
"""Constructor.

:param opt_problem: optimization problem on which Lagrange multipliers
shall be computed
"""
self._check_inputs(opt_problem)
self.opt_problem = opt_problem
self.active_lb_names = []
self.active_ub_names = []
self.active_ineq_names = []
self.active_eq_names = []
self.lagrange_multipliers = None
self.__normalized = opt_problem.preprocess_options.get("normalize", False)

@staticmethod
def _check_inputs(opt_problem):
"""
Checks inputs : verify that opt_problem is an
instance of OptimizationProblem

:param opt_problem: optimization problem on which Lagrange multipliers
shall be computed
"""
if not isinstance(opt_problem, OptimizationProblem):
raise ValueError(
"Argument of LagrangeMultiplier class"
+ " has to be an instance of OptimizationProblem"
)
if opt_problem.solution is None:
raise ValueError("Optimization problem was not solved !")

[docs]    def compute(self, x_vect, ineq_tolerance=1e-6, rcond=-1):
"""Computes and returns the Lagrange multipliers, as a post-processing of the
optimal point.

This solves :
(d ActiveConstraints)'               d Objective
(-------------------)  . Lambda = -  -----------
(d X                )                d X

:param x_vect: x point on which the multipliers shall be computed
:param ineq_tolerance: tolerance on inequality constraints
:param rcond: float, optional
Cut-off ratio for small singular values of the jacobian.
see sipy.linalg.lsq
"""
LOGGER.info("Computation of Lagrange multipliers")

# Check feasibility
self._check_feasibility(x_vect)

# get jacobian of all active constraints, and an
# ordered list of their name
jac_act, _ = self._get_jac_act(x_vect, ineq_tolerance)
if jac_act is None:
# There is no active constraint
multipliers = []
self._store_multipliers(multipliers)
return self.lagrange_multipliers
lhs = jac_act.T
act_constr_nb = lhs.shape[1]
rank = matrix_rank(lhs)
LOGGER.info("Found %s active constraints", str(act_constr_nb))
LOGGER.info("Rank of jacobian = %s", str(rank))
if act_constr_nb > rank:
LOGGER.warning("Number of active constraints > rank !")

# get jacobian of objective
obj_jac = self._get_obj_jac(x_vect)
rhs = -obj_jac.T

# Compute the Lagrange multipliers as a feasible solution of a
# linear optimization problem
act_eq_constr_nb = len(self.active_eq_names)
bounds = [(0, None)] * (act_constr_nb - act_eq_constr_nb) + [
(None, None)
] * act_eq_constr_nb
optim_result = linprog(zeros(act_constr_nb), A_eq=lhs, b_eq=rhs, bounds=bounds)
if optim_result.status == 2:
LOGGER.warning("Lagrange multipliers appear not to exist")
if optim_result.success:
mul = optim_result.x
else:
# If the linear optimization failed then obtain the Lagrange
# multipliers as a solution of a least-square problem
mul, residuals, _, sval = lstsq(lhs, rhs, rcond=rcond)
LOGGER.info("Min singular values of jacobian = %s", str(sval.min()))
LOGGER.info("Residuals norm = %s", str(norm(residuals)))

# stores multipliers in a dictionary
self._store_multipliers(mul)

return self.lagrange_multipliers

def _check_feasibility(self, x_vect):
"""Checks that the given point is in the design space and satisfies all
constraints.

:param x_vect: point at which the Lagrange multipliers are to be
computed
:type x_vect: ndarray
"""
problem = self.opt_problem

# Check that the point is within bounds
problem.design_space.check_membership(x_vect)

# Check that the point satisfies other constraints
values, _ = problem.evaluate_functions(
x_vect, eval_obj=False, normalize=False, no_db_no_norm=True
)
feasible = problem.is_point_feasible(values)
if not feasible:
LOGGER.warning("Infeasible point, Lagrange multipliers may not" " exist")

def _get_act_bound_jac(self, act_bounds):
"""Returns the jacobian of active  bounds constraints sign is not taken into
account (matrix is made of 0 and 1)"""
dspace = self.opt_problem.design_space
x_dim = dspace.dimension
dim_act = sum(len(where(bnd)[0]) for bnd in act_bounds.values())
if dim_act == 0:
return None, []
act_array = concatenate([act_bounds[var] for var in dspace.variables_names])

bnd_jac = zeros((dim_act, x_dim))

if self.__normalized:
norm_factor = dspace.get_upper_bounds() - dspace.get_lower_bounds()
act_jac = norm_factor[act_array]
else:
act_jac = 1.0
bnd_jac[arange(dim_act), act_array] = act_jac
indexed_varnames = array(dspace.get_indexed_variables_names())
act_b_names = indexed_varnames[act_array].tolist()
return bnd_jac, act_b_names

def __get_act_ineq_jac(self, x_vect, ineq_tolerance=1e-6):
"""Returns the jacobian of active inequality constraints defined by user in the
optimization problem."""
# retrieves the active functions and the indices :
# a function is active if at least
# one of its component (in case of multidimensional constraints) is
# active
act_func = self.opt_problem.get_active_ineq_constraints(x_vect, ineq_tolerance)

dspace = self.opt_problem.design_space

if self.__normalized:
x_vect = dspace.normalize_vect(x_vect)
jac = []
names = []

for func, act_set in act_func.items():
if True in act_set:
ineq_jac = func.jac(x_vect)
if len(ineq_jac.shape) == 1:
# Make sure the Jacobian is a 2-dimensional array
ineq_jac = ineq_jac.reshape((1, x_vect.size))
else:
ineq_jac = ineq_jac[act_set, :]
jac.append(ineq_jac)
if func.dim == 1:
names.append(func.name)
else:
names += [
self._get_component_name(func.name, i)
for i, active in enumerate(act_set)
if active
]
if jac:
jac = concatenate(jac)
else:
jac = None
return jac, names

def _get_act_eq_jac(self, x_vect):
"""Returns jacobian of active equality constraints defined by user in the
optimization problem."""
eq_functions = self.opt_problem.get_eq_constraints()
# loop on equality functions
# NB: as the solution (x_vect) is supposed to be feasible,
# all functions (on all dimensions) are supposed to be active
jac = []
names = []
dspace = self.opt_problem.design_space

if self.__normalized:
x_vect = dspace.normalize_vect(x_vect)

for eq_function in eq_functions:
eq_jac = atleast_2d(eq_function.jac(x_vect))
jac.append(eq_jac)
if eq_function.dim == 1:
names.append(eq_function.name)
else:
names += [
self._get_component_name(eq_function.name, i)
for i in range(eq_jac.shape[0])
]
if jac:
jac = concatenate(jac)
else:
jac = None
return jac, names

def _get_obj_jac(self, x_vect):
"""Returns objective jacobian."""
if self.__normalized:
x_vect = self.opt_problem.design_space.normalize_vect(x_vect)

return self.opt_problem.objective.jac(x_vect)

def _get_jac_act(self, x_vect, ineq_tolerance=1e-6):
"""Returns active constraints jacobian, and the name of each component of each
function."""
# Bounds jacobian
dspace = self.opt_problem.design_space
act_lb, act_ub = dspace.get_active_bounds(x_vect, tol=ineq_tolerance)
lb_jac_act, self.active_lb_names = self._get_act_bound_jac(act_lb)
if lb_jac_act is not None:
lb_jac_act *= -1
ub_jac_act, self.active_ub_names = self._get_act_bound_jac(act_ub)

# inequality names
tol = ineq_tolerance
ineq_jac, self.active_ineq_names = self.__get_act_ineq_jac(x_vect, tol)
# equality names
eq_jac, eq_names_act = self._get_act_eq_jac(x_vect)
self.active_eq_names = eq_names_act

names_list = (
self.active_lb_names
+ self.active_ub_names
+ self.active_ineq_names
+ eq_names_act
)
jac_list = [lb_jac_act, ub_jac_act, ineq_jac, eq_jac]
jac_list = [jac for jac in jac_list if jac is not None]
if jac_list:
jac_act_arr = concatenate(jac_list, axis=0)
else:
# There no active constraint
jac_act_arr = None

return jac_act_arr, names_list

def _store_multipliers(self, multipliers):
"""Stores multipliers in a dictionary."""
lag = {}

i_min = 0
n_act = len(self.active_lb_names)
if n_act > 0:
l_b_mult = multipliers[i_min : i_min + n_act]
lag[self.LOWER_BOUNDS] = (self.active_lb_names, l_b_mult)
i_min += n_act
wrong_inds = where(l_b_mult < 0.0)[0]
if wrong_inds.size > 0:
names_neg = array(self.active_lb_names)[wrong_inds]
LOGGER.warning(
"Negative Lagrange multipliers for "
"lower bounds on variables"
"%s !",
str(names_neg),
)
n_act = len(self.active_ub_names)
if n_act > 0:
u_b_mult = multipliers[i_min : i_min + n_act]
lag[self.UPPER_BOUNDS] = (self.active_ub_names, u_b_mult)
i_min += n_act
wrong_inds = where(u_b_mult < 0.0)[0]
if wrong_inds.size > 0:
names_neg = array(self.active_ub_names)[wrong_inds]
LOGGER.warning(
"Negative Lagrange multipliers for "
"upper bounds on variables"
"%s !",
str(names_neg),
)
n_act = len(self.active_ineq_names)
if n_act > 0:
ineq_mult = multipliers[i_min : i_min + n_act]
lag[self.INEQUALITY] = (self.active_ineq_names, ineq_mult)
i_min += n_act
wrong_inds = where(ineq_mult < 0.0)[0]
if wrong_inds.size > 0:
names_neg = array(self.active_ineq_names)[wrong_inds]
LOGGER.warning(
"Negative Lagrange multipliers for "
"inequality constraints"
"%s !",
str(names_neg),
)
if self.active_eq_names:
lag[self.EQUALITY] = (
self.active_eq_names,
multipliers[i_min : i_min + n_act],
)
i_min += n_act

self.lagrange_multipliers = lag

def _initialize_multipliers(self):
"""Initialize the Lagrange multipliers with zeros.

:returns: Lagrange multipliers
:rtype: dict(dict(float64))
"""
problem = self.opt_problem
multipliers = dict()

# Bound-constraints
indexed_varnames = problem.design_space.get_indexed_variables_names()
multipliers[self.LOWER_BOUNDS] = dict.fromkeys(indexed_varnames, 0.0)
multipliers[self.UPPER_BOUNDS] = dict.fromkeys(indexed_varnames, 0.0)

# Inequality-constraints
multipliers[self.INEQUALITY] = {
func.name if func.dim == 1 else self._get_component_name(func.name, i): 0.0
for func in problem.get_ineq_constraints()
for i in range(func.dim)
}

# Equality-constraints
multipliers[self.EQUALITY] = {
func.name if func.dim == 1 else self._get_component_name(func.name, i): 0.0
for func in problem.get_eq_constraints()
for i in range(func.dim)
}

return multipliers

[docs]    def get_multipliers_arrays(self):
"""Return the Lagrange multipliers (zero and nonzero) as arrays.

:returns: Lagrange multipliers
:rtype: dict(dict(ndarray))
"""
problem = self.opt_problem
design_space = problem.design_space

# Convert to dictionaries
multipliers = dict()
for label in self.CSTR_LABELS:
names, mults = self.lagrange_multipliers.get(label, ([], array([])))
multipliers[label] = dict(zip(names, mults))

# Add the Lagrange multipliers equal to zero
multipliers_init = self._initialize_multipliers()
for label in self.CSTR_LABELS:
multipliers_init[label].update(multipliers[label])

# Cast the multipliers as arrays
mult_arrays = dict()
# Bound-constraints multipliers
mult_arrays[self.LOWER_BOUNDS] = dict()
mult_arrays[self.UPPER_BOUNDS] = dict()
for name in design_space.variables_names:
indexed_varnames = design_space.get_indexed_variables_names()
var_low_mult = array(
[
multipliers_init[self.LOWER_BOUNDS][comp_name]
for comp_name in indexed_varnames
]
)
mult_arrays[self.LOWER_BOUNDS][name] = var_low_mult
var_upp_mult = array(
[
multipliers_init[self.UPPER_BOUNDS][comp_name]
for comp_name in indexed_varnames
]
)
mult_arrays[self.UPPER_BOUNDS][name] = var_upp_mult
# Inequality-constraints multipliers
ineq_mult = multipliers_init[self.INEQUALITY]
mult_arrays[self.INEQUALITY] = dict()
for func in problem.get_ineq_constraints():
func_mult = array(
[
ineq_mult[
func.name
if func.dim == 1
else self._get_component_name(func.name, index)
]
for index in range(func.dim)
]
)
mult_arrays[self.INEQUALITY][func.name] = func_mult
# Equality-constraints multipliers
eq_mult = multipliers_init[self.EQUALITY]
mult_arrays[self.EQUALITY] = dict()
for func in problem.get_eq_constraints():
func_mult = array(
[
eq_mult[
func.name
if func.dim == 1
else self._get_component_name(func.name, index)
]
for index in range(func.dim)
]
)
mult_arrays[self.EQUALITY][func.name] = func_mult

return mult_arrays

@staticmethod
def _get_component_name(name, index):
"""Return the name of a variable component.

:param name: the variable name
:type name: str
:param index: the component index
:type index: int
:returns: name of the variable component
:rtype: str
"""
return name + DesignSpace.SEP + str(index)

def _get_pretty_table(self):
"""Displays Lagrange Multipliers."""
table = PrettyTable(
["Constraint type", "Constraint name", "Lagrange Multiplier"]
)

for cstr_type, nam_val in self.lagrange_multipliers.items():
for name, value in zip(nam_val[0], nam_val[1]):