NonNegativeTransformation
(class from pyomo.core.plugins.transform.nonnegative_transform)
- class pyomo.core.plugins.transform.nonnegative_transform.NonNegativeTransformation(**kwds)[source]
Bases:
IsomorphicTransformationCreates a new, equivalent model by forcing all variables to lie in the nonnegative orthant by introducing auxiliary variables.
Methods
__init__(**kwds)apply(model, **kwds)DEPRECATED.
apply_to(model, **kwds)Apply the transformation to the given model.
boundsConstraintRule(lb, ub, attr, vars, model)Produces 'lb < x^+ - x^- < ub' style constraints.
create_using(model, **kwds)Create a new model with this transformation
delayedExprMapRule(ruleMap, model[, ndx])Rule intended to return expressions from a lookup table.
exprMapRule(ruleMap, model[, ndx])Rule intended to return expressions from a lookup table
noConstraint(*args)sumRule(attr, vars, model)Returns a sum expression.
Member Documentation
- apply(model, **kwds)
DEPRECATED.
Deprecated since version 4.3.11323: Transformation.apply() has been deprecated. Please use either Transformation.apply_to() for in-place transformations or Transformation.create_using() for transformations that create a new, independent transformed model instance.
- apply_to(model, **kwds)
Apply the transformation to the given model.
- static boundsConstraintRule(lb, ub, attr, vars, model)[source]
Produces ‘lb < x^+ - x^- < ub’ style constraints. Designed to be made a closer through functools.partial, across lb, ub, attr, and vars. vars is a {varname: coefficient} dictionary. attr is the base variable name; that is, X[1] would be referenced by
model.__getattribute__(‘X’)[1]
and so attr=’X’, and 1 is a key of vars.
- create_using(model, **kwds)
Create a new model with this transformation
- static delayedExprMapRule(ruleMap, model, ndx=None)[source]
Rule intended to return expressions from a lookup table. Each entry in the lookup table is a functor that needs to be evaluated before returning.