def define_model(**kwds):
sense = kwds.pop("sense")
m = pmo.block()
m.x = pmo.variable_list()
m.Fx = pmo.variable_list()
m.piecewise = pmo.block_list()
for i in range(4):
m.x.append(pmo.variable(lb=0, ub=3))
m.Fx.append(pmo.variable())
m.piecewise.append(
pmo.piecewise(breakpoints, values,
input=m.x[i],
output=m.Fx[i],
**kwds))
m.obj = pmo.objective(expr=sum(m.Fx) + sum(m.x),
sense=sense)
# fix the answer for testing purposes
m.set_answer = pmo.constraint_list()
# Fx1 should solve to 0
m.set_answer.append(pmo.constraint(expr= m.x[0] == 0.5))
m.set_answer.append(pmo.constraint(expr= m.x[1] == 1.0))
m.set_answer.append(pmo.constraint(expr= m.Fx[1] == 0.5))
# Fx[2] should solve to 1
m.set_answer.append(pmo.constraint(expr= m.x[2] == 1.5))
# Fx[3] should solve to 1.5
m.set_answer.append(pmo.constraint(expr= m.x[3] == 2.5))
return m
def define_model(**kwds):
sense = kwds.pop("sense")
m = pmo.block()
m.x = pmo.variable_list()
m.Fx = pmo.variable_list()
m.piecewise = pmo.block_list()
for i in range(7):
m.x.append(pmo.variable(lb=-5, ub=4))
m.Fx.append(pmo.variable())
m.piecewise.append(
pmo.piecewise(breakpoints, values,
input=m.x[i],
output=m.Fx[i],
**kwds))
m.obj = pmo.objective(expr=sum(m.Fx),
sense=sense)
# fix the answer for testing purposes
m.set_answer = pmo.constraint_list()
m.set_answer.append(pmo.constraint(m.x[0] == -5.0))
m.set_answer.append(pmo.constraint(m.x[1] == -3.0))
m.set_answer.append(pmo.constraint(m.x[2] == -2.5))
m.set_answer.append(pmo.constraint(m.x[3] == -1.5))
m.set_answer.append(pmo.constraint(m.x[4] == 2.0))
m.set_answer.append(pmo.constraint(m.x[5] == 3.5))
m.set_answer.append(pmo.constraint(m.x[6] == 4.0))
return m
def define_model(**kwds):
sense = kwds.pop("sense")
m = pmo.block()
m.x = pmo.variable_list()
m.Fx = pmo.variable_list()
m.piecewise = pmo.block_list()
for i in range(7):
m.x.append(pmo.variable(lb=-5, ub=4))
m.Fx.append(pmo.variable())
m.piecewise.append(
pmo.piecewise(breakpoints, values,
input=m.x[i],
output=m.Fx[i],
**kwds))
m.obj = pmo.objective(expr=sum(m.Fx),
sense=sense)
# fix the answer for testing purposes
m.set_answer = pmo.constraint_list()
m.set_answer.append(pmo.constraint(m.x[0] == -5.0))
m.set_answer.append(pmo.constraint(m.x[1] == -3.0))
m.set_answer.append(pmo.constraint(m.x[2] == -2.5))
m.set_answer.append(pmo.constraint(m.x[3] == -1.5))
m.set_answer.append(pmo.constraint(m.x[4] == 2.0))
m.set_answer.append(pmo.constraint(m.x[5] == 3.5))
m.set_answer.append(pmo.constraint(m.x[6] == 4.0))
return m
def define_model(**kwds):
sense = kwds.pop("sense")
m = block()
m.x = variable_list()
m.Fx = variable_list()
m.piecewise = block_list()
for i in range(4):
m.x.append(variable(lb=0, ub=6))
m.Fx.append(variable())
m.piecewise.append(
piecewise(breakpoints, values,
input=m.x[i],
output=m.Fx[i],
**kwds))
m.obj = objective(expr=sum(m.Fx),
sense=sense)
# fix the answer for testing purposes
m.set_answer = constraint_list()
m.set_answer.append(constraint(m.x[0] == 0.0))
m.set_answer.append(constraint(m.x[1] == 3.0))
m.set_answer.append(constraint(m.x[2] == 5.5))
m.set_answer.append(constraint(m.x[3] == 6.0))
return m
def test_component_map_hack(self):
m = pmo.block()
m.v = pmo.variable()
m.c = pmo.constraint()
m.B = pmo.block_list()
m.B.append(pmo.block())
m.B[0].v = pmo.variable()
m.B[0].c = pmo.constraint()
m.b = pmo.block()
m.b.v = pmo.variable()
m.b.c = pmo.constraint()
self.assertTrue(type(m.component_map()) == dict)
def build_block_list_with_variables():
blist = block_list()
for i in range(N):
b = block()
b._activate_large_storage_mode()
b.x1 = variable(domain_type=None, lb=None, ub=None)
b.x2 = variable(domain_type=None, lb=None, ub=None)
b.x3 = variable(domain_type=None, lb=None, ub=None)
b.x4 = variable(domain_type=None, lb=None, ub=None)
b.x5 = variable(domain_type=None, lb=None, ub=None)
blist.append(b)
return blist
def build_block_list_with_variables():
blist = block_list()
for i in range(N):
b = block()
b._activate_large_storage_mode()
b.x1 = variable(domain_type=None, lb=None, ub=None)
b.x2 = variable(domain_type=None, lb=None, ub=None)
b.x3 = variable(domain_type=None, lb=None, ub=None)
b.x4 = variable(domain_type=None, lb=None, ub=None)
b.x5 = variable(domain_type=None, lb=None, ub=None)
blist.append(b)
return blist
def build_block_list_with_variables():
blist = block_list()
for i in range(N):
b = block()
b.x1 = variable(domain_type=None, lb=None, ub=None)
b.x2 = variable(domain_type=None, lb=None, ub=None)
b.x3 = variable(domain_type=None, lb=None, ub=None)
b.x4 = variable(domain_type=None, lb=None, ub=None)
b.x5 = variable(domain_type=None, lb=None, ub=None)
b.x6 = variable(domain_type=None, lb=None, ub=None)
b.x7 = variable(domain_type=None, lb=None, ub=None)
b.x8 = variable(domain_type=None, lb=None, ub=None)
blist.append(b)
return blist
def test_component_objects_hack(self):
m = pmo.block()
m.v = pmo.variable()
m.c = pmo.constraint()
m.B = pmo.block_list()
m.B.append(pmo.block())
m.B[0].v = pmo.variable()
m.B[0].c = pmo.constraint()
m.b = pmo.block()
m.b.v = pmo.variable()
m.b.c = pmo.constraint()
for obj1, obj2 in zip(m.components(),
m.component_objects()):
self.assertIs(obj1, obj2)
def build_small_block_list_with_staticvariables():
return block_list(build_small_block_list_with_staticvariables.myblock()
for i in range(N))
def build_tiny_block_list_with_variables():
return block_list(build_tiny_block_list_with_variables.myblock()
for i in range(N))
_ctype = IJunk _model = pmo.block() _model.v = pmo.variable() _model.V = pmo.variable_list() _model.V.append(pmo.variable()) _model.V.append(pmo.variable_list()) _model.V[1].append(pmo.variable()) _model.c = pmo.constraint() _model.C = pmo.constraint_list() _model.C.append(pmo.constraint()) _model.C.append(pmo.constraint_list()) _model.C[1].append(pmo.constraint()) b_clone = _model.clone() _model.b = b_clone.clone() _model.B = pmo.block_list() _model.B.append(b_clone.clone()) _model.B.append(pmo.block_list()) _model.B[1].append(b_clone.clone()) del b_clone _model.j = junk() _model.J = junk_list() _model.J.append(junk()) _model.J.append(junk_list()) _model.J[1].append(junk()) _model.J[1][0].b = pmo.block() _model.J[1][0].b.v = pmo.variable() model_clone = _model.clone() _model.k = pmo.block() _model.K = pmo.block_list() _model.K.append(model_clone.clone())
def build_small_block_list_with_staticvariables():
return block_list(
build_small_block_list_with_staticvariables.myblock()
for i in range(N))
#
vd = pmo.variable_dict(
((str(i), pmo.variable()) for i in range(10)))
cd = pmo.constraint_dict(
(i, pmo.constraint(v == 1)) for i,v in vd.items())
cd = pmo.constraint_dict()
for i, v in vd.items():
cd[i] = pmo.constraint(v == 1)
cd = pmo.constraint_dict()
cd.update((i, pmo.constraint()) for i,v in vd.items())
cd[None] = pmo.constraint()
del cd[None]
#
# Nesting containers
#
b = pmo.block()
b.bd = pmo.block_dict()
b.bd[None] = pmo.block_dict()
b.bd[None][1] = pmo.block()
b.bd[None][1].x = pmo.variable()
b.bd['a'] = pmo.block_list()
b.bd['a'].append(pmo.block())
_model = pmo.block() _model.v = pmo.variable() _model.V = pmo.variable_list() _model.V.append(pmo.variable()) _model.V.append(pmo.variable_list()) _model.V[1].append(pmo.variable()) _model.c = pmo.constraint() _model.C = pmo.constraint_list() _model.C.append(pmo.constraint()) _model.C.append(pmo.constraint_list()) _model.C[1].append(pmo.constraint()) b_clone = _model.clone() _model.b = b_clone.clone() _model.B = pmo.block_list() _model.B.append(b_clone.clone()) _model.B.append(pmo.block_list()) _model.B[1].append(b_clone.clone()) del b_clone _model.j = junk() _model.J = junk_list() _model.J.append(junk()) _model.J.append(junk_list()) _model.J[1].append(junk()) _model.J[1][0].b = pmo.block() _model.J[1][0].b.v = pmo.variable() model_clone = _model.clone() _model.k = pmo.block() _model.K = pmo.block_list() _model.K.append(model_clone.clone())
b.c = pmo.constraint(b.x >= 1)
b.clist = pmo.constraint_list(
pmo.constraint(b.xlist[i] >= i) for i in range(10))
b.cdict = pmo.constraint_dict(((i, j), pmo.constraint(b.xdict[i, j] >= i * j))
for i in range(10) for j in range(10))
b.o = pmo.objective(b.x + sum(b.xlist) + sum(b.xdict.values()))
#
# Define a custom tiny_block
#
# The tiny_block class uses more efficient storage for the
# case when a small number of objects are placed on the block.
class Widget(pmo.tiny_block):
def __init__(self, p, input=None):
super(Widget, self).__init__()
self.p = pmo.parameter(value=p)
self.input = pmo.expression(expr=input)
self.output = pmo.variable()
self.c = pmo.constraint(self.output == self.input**2 / self.p)
b = pmo.block()
b.x = pmo.variable()
b.widgets = pmo.block_list()
for i in range(10):
b.widgets.append(Widget(i, input=b.x))
#
# uses OrderedDict when ordered=True
vd = pmo.variable_dict(((str(i), pmo.variable()) for i in range(10)),
ordered=True)
cd = pmo.constraint_dict((i, pmo.constraint(v == 1)) for i, v in vd.items())
cd = pmo.constraint_dict()
for i, v in vd.items():
cd[i] = pmo.constraint(v == 1)
cd = pmo.constraint_dict()
cd.update((i, pmo.constraint()) for i, v in vd.items())
cd[None] = pmo.constraint()
del cd[None]
#
# Nesting containers
#
b = pmo.block()
b.bd = pmo.block_dict()
b.bd[None] = pmo.block_dict()
b.bd[None][1] = pmo.block()
b.bd[None][1].x = pmo.variable()
b.bd['a'] = pmo.block_list()
b.bd['a'].append(pmo.block())
