# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright (c) 2008-2022
# National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
"""Transformation to propagate a zero value to terms of a sum."""
from pyomo.core.base.transformation import TransformationFactory
from pyomo.core.base.constraint import Constraint
from pyomo.core.expr.numvalue import value
from pyomo.core.plugins.transform.hierarchy import IsomorphicTransformation
from pyomo.repn.standard_repn import generate_standard_repn
[docs]@TransformationFactory.register('contrib.propagate_zero_sum',
doc="Propagate fixed-to-zero for sums of only positive (or negative) vars.")
class ZeroSumPropagator(IsomorphicTransformation):
"""Propagates fixed-to-zero for sums of only positive (or negative) vars.
If :math:`z` is fixed to zero and :math:`z = x_1 + x_2 + x_3` and
:math:`x_1`, :math:`x_2`, :math:`x_3` are all non-negative or all
non-positive, then :math:`x_1`, :math:`x_2`, and :math:`x_3` will be fixed
to zero.
"""
def _apply_to(self, instance):
for constr in instance.component_data_objects(ctype=Constraint,
active=True,
descend_into=True):
if not constr.body.polynomial_degree() == 1:
continue # constraint not linear. Skip.
repn = generate_standard_repn(constr.body)
if (constr.has_ub() and (
(repn.constant is None and value(constr.upper) == 0) or
repn.constant == value(constr.upper)
)):
# term1 + term2 + term3 + ... <= 0
# all var terms need to be non-negative
if all(
# variable has 0 coefficient
coef == 0 or
# variable is non-negative and has non-negative coefficient
(repn.linear_vars[i].has_lb() and
value(repn.linear_vars[i].lb) >= 0 and
coef >= 0) or
# variable is non-positive and has non-positive coefficient
(repn.linear_vars[i].has_ub() and
value(repn.linear_vars[i].ub) <= 0 and
coef <= 0) for i, coef in enumerate(repn.linear_coefs)):
for i, coef in enumerate(repn.linear_coefs):
if not coef == 0:
repn.linear_vars[i].fix(0)
continue
if (constr.has_lb() and (
(repn.constant is None and value(constr.lower) == 0) or
repn.constant == value(constr.lower)
)):
# term1 + term2 + term3 + ... >= 0
# all var terms need to be non-positive
if all(
# variable has 0 coefficient
coef == 0 or
# variable is non-negative and has non-positive coefficient
(repn.linear_vars[i].has_lb() and
value(repn.linear_vars[i].lb) >= 0 and
coef <= 0) or
# variable is non-positive and has non-negative coefficient
(repn.linear_vars[i].has_ub() and
value(repn.linear_vars[i].ub) <= 0 and
coef >= 0) for i, coef in enumerate(repn.linear_coefs)):
for i, coef in enumerate(repn.linear_coefs):
if not coef == 0:
repn.linear_vars[i].fix(0)