# The ASL differentiation routines seem to have a
# bug that causes the lagrangian hessian to become
# dense unless this constant term in moved to the
# numerator.
#
# This test model relies on the gjh_asl_json executable. It
# will not solve if sent to a real optimizer.
#
from pyomo.environ import ConcreteModel, Var, Objective, Constraint
model = ConcreteModel()
model.x = Var(bounds=(-1.0, 1.0), initialize=1.0)
model.y = Var(bounds=(-1.0, 1.0), initialize=2.0)
model.v = Var(bounds=(-1.0, 1.0), initialize=3.0)
model.p = Var(initialize=2.0)
model.p.fixed = True
model.OBJ = Objective(expr=model.x)
model.CON1 = Constraint(
rule=lambda model: (2.0, 1.0 / model.p * model.v * (model.x - model.y)))
model.CON2 = Constraint(expr=model.v * 1.0 / model.p *
(model.x - model.y) == 2.0)
model.CON3 = Constraint(expr=model.v * (model.x - model.y) / model.p == 2.0)
model.CON4 = Constraint(expr=model.v *
(model.x / model.p - model.y / model.p) == 2.0)
model.CON5 = Constraint(expr=model.v * (model.x - model.y) *
(1.0 / model.p) == 2.0)
model.CON6 = Constraint(expr=model.v * (model.x - model.y) -
2.0 * model.p == 0)
# dense unless this constant term in moved to the # numerator. # # This test model relies on the gjh_asl_json executable. It # will not solve if sent to a real optimizer. # from pyomo.environ import ConcreteModel, Var, Param, Objective, Constraint model = ConcreteModel() model.x = Var(bounds=(-1.0,1.0),initialize=1.0) model.y = Var(bounds=(-1.0,1.0),initialize=2.0) model.v = Var(bounds=(-1.0,1.0),initialize=3.0) model.p = Param(initialize=2.0) model.q = Param(initialize=2.0,mutable=True) model.OBJ = Objective(expr=model.x**2/model.p + model.x**2/model.q) model.CON1 = Constraint(expr=1.0/model.p*model.v*(model.x-model.y) == 2.0) model.CON2 = Constraint(expr=model.v*1.0/model.p*(model.x-model.y) == 2.0) model.CON3 = Constraint(expr=model.v*(model.x-model.y)/model.p == 2.0) model.CON4 = Constraint(expr=model.v*(model.x/model.p-model.y/model.p) == 2.0) model.CON5 = Constraint(expr=model.v*(model.x-model.y)*(1.0/model.p) == 2.0) model.CON6 = Constraint(expr=model.v*(model.x-model.y) == 2.0*model.p) model.CON7 = Constraint(expr=1.0/model.q*model.v*(model.x-model.y) == 2.0) model.CON8 = Constraint(expr=model.v*1.0/model.q*(model.x-model.y) == 2.0) model.CON9 = Constraint(expr=model.v*(model.x-model.y)/model.q == 2.0) model.CON10 = Constraint(expr=model.v*(model.x/model.q-model.y/model.q) == 2.0) model.CON11 = Constraint(expr=model.v*(model.x-model.y)*(1.0/model.q) == 2.0) model.CON12 = Constraint(expr=model.v*(model.x-model.y) == 2.0*model.q)