def makeTwoTermIndexedDisjunction():
m = ConcreteModel()
m.A = Set(initialize=[1, 2, 3])
m.B = Set(initialize=['a', 'b'])
m.x = Var(m.A, bounds=(-10, 10))
def disjunct_rule(d, i, k):
m = d.model()
if k == 'a':
d.cons_a = Constraint(expr=m.x[i] >= 5)
if k == 'b':
d.cons_b = Constraint(expr=m.x[i] <= 0)
m.disjunct = Disjunct(m.A, m.B, rule=disjunct_rule)
def disj_rule(m, i):
return [m.disjunct[i, k] for k in m.B]
m.disjunction = Disjunction(m.A, rule=disj_rule)
return m
def get_model(flag, fixprim, fixdual):
model = ConcreteModel()
model.obj = Param(default=20)
model.n = Param(default=7)
model.m = Param(default=7)
model.N = RangeSet(1, model.n)
model.M = RangeSet(1, model.m)
def c_rule(model, j):
return 5 if j < 5 else 9.0 / 2
model.c = Param(model.N)
def b_rule(model, i):
if i == 4:
i = 5
elif i == 5:
i = 4
return 5 if i < 5 else 7.0 / 2
model.b = Param(model.M)
def A_rule(model, i, j):
if i == 4:
i = 5
elif i == 5:
i = 4
return 2 if i == j else 1
model.A = Param(model.M, model.N)
model.x = Var(model.N, within=NonNegativeReals)
model.y = Var(model.M, within=NonNegativeReals)
model.xx = Var([1, 2], model.N, within=NonNegativeReals)
model.yy = Var([1, 2], model.M, within=NonNegativeReals)
if flag:
model.ydiff = Objective(expr=model.yy[2, fixdual] -
model.yy[1, fixdual])
def yext_rule(model, k):
return sum(model.b[i] * model.yy[k, i]
for i in model.M) == model.obj
model.yext = Constraint([1, 2])
def dualcons_rule(model, k, j):
return sum(model.A[i, j] * model.yy[k, i]
for i in model.N) <= model.c[j]
model.dualcons = Constraint([1, 2], model.N)
else:
model.xdiff = Objective(expr=model.xx[2, fixprim] -
model.xx[1, fixprim])
def xext_rule(model, k):
return sum(model.c[j] * model.xx[k, j]
for j in model.N) == model.obj
model.xext = Constraint([1, 2])
def primcons_rule(model, k, i):
return sum(model.A[i, j] * model.xx[k, j]
for j in model.M) >= model.b[i]
model.primcons = Constraint([1, 2], model.M)
model.create()
return model
i = 4
return 5 if i < 5 else 7.0 / 2
model.b = Param(model.M)
def A_rule(model, i, j):
if i == 4:
i = 5
elif i == 5:
i = 4
return 2 if i == j else 1
model.A = Param(model.M, model.N)
model.x = Var(model.N, within=NonNegativeReals)
model.y = Var(model.M, within=NonNegativeReals)
model.cost = Objective(expr=sum_product(model.c, model.x))
def primalcon_rule(model, i):
return sum(model.A[i, j] * model.x[j] for j in model.N) >= model.b[i]
model.primalcon = Constraint(model.M)
model.dual = Suffix(direction=Suffix.IMPORT)
model.rc = Suffix(direction=Suffix.IMPORT)
