Source code for pyomo.contrib.preprocessing.plugins.zero_sum_propagator

#  ___________________________________________________________________________
#
#  Pyomo: Python Optimization Modeling Objects
#  Copyright 2017 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.plugin 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)