def test_le_cnt_with_opt():
prob = OptProb()
model = prob.get_model()
param = TestParam("test", 3, 1)
param.resample()
var = Variable(model, param)
prob.add_var(var)
twoe1 = AffExpr(constant=np.array([[2], [0], [-1]], dtype=np.float))
# var = Variable(model, param)
lhs = AffExpr({param: 1})
fluent = LinLEFluent('test_eq', 0, lhs, twoe1)
variables = [var]
param_to_var = {param: var}
constraints = Constraints(model)
if isinstance(fluent, LinLEFluent):
lhs = to_gurobi_expr(fluent.lhs, param_to_var)
rhs = to_gurobi_expr(fluent.rhs, param_to_var)
model.update()
constraints.add_leq_cntr(lhs, rhs)
assert not fluent.satisfied()
prob.add_constraints(constraints)
obj = grb.QuadExpr()
for var in variables:
for i in range(var.rows):
for j in range(var.cols):
v = var.grb_vars[i, j]
obj += v*v - 2*v + 1
prob.obj_sqp = obj
prob.optimize()
var.update_hl_param()
assert fluent.satisfied()
assert np.all(variables[0].get_val() == np.array([[1], [0], [-1]]))
def test_eq_cnt_with_gurobi():
model = grb.Model()
obj = grb.QuadExpr()
param = TestParam("test", 3, 1)
param.resample()
var = Variable(model, param)
zero = AffExpr(constant=np.array([[0], [0], [0]], dtype=np.float))
# var = Variable(model, param)
lhs = AffExpr({param: 1})
fluent = LinEqFluent('test_eq', 0, lhs, zero)
variables = [Variable(model, param)]
param_to_var = {param: variables[0]}
constraints = Constraints(model)
if isinstance(fluent, LinEqFluent):
lhs = to_gurobi_expr(fluent.lhs, param_to_var)
rhs = to_gurobi_expr(fluent.rhs, param_to_var)
model.update()
constraints.add_eq_cntr(lhs, rhs)
assert not fluent.satisfied()
for var in variables:
for i in range(var.rows):
for j in range(var.cols):
v = var.grb_vars[i, j]
obj += v*v - 2*v + 1
model.setObjective(obj)
model.update()
model.optimize()
var.update()
var.update_hl_param()
assert fluent.satisfied()