def bad_eq_lp_problem():
LE_A = -1 * torch.DoubleTensor([
[1, 0, 0, 1], # >= 1
[0, 1, 0, 1], # >= 1
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
])
LE_B = -1 * torch.DoubleTensor([1, 1, 0, 0, 0, 0])
EQ_A = torch.DoubleTensor([
[1, 1, 0, 0], #==1
[1, 1, 0, 0], #==2
])
EQ_B = torch.DoubleTensor([1, 2])
OBJ_C = torch.DoubleTensor([2, 3, 1, 5])
eq = LinearEqConstraints(EQ_A, EQ_B)
le = LinearLeConstraints(LE_A, LE_B)
obj = LinearObjective(OBJ_C)
_vars = ["x_%s" % i for i in range(4)]
return Problem(_vars, obj, le, eq)
def easy_qp_problem():
LE_A = -1 * torch.DoubleTensor([[1, 1, 0, 0], [0, 0, 1, 1]])
LE_B = -1 * torch.DoubleTensor([1, 1])
# EQ_A = -1 * torch.DoubleTensor([[0,0,0,0],
# [0,0,1,1]])
# EQ_B = -1 * torch.DoubleTensor([1,1])
OBJ_A = torch.DoubleTensor(np.diag([1, 1, 1, 1]))
OBJ_B = torch.DoubleTensor([1, 1, 1, 1])
eq = None
# eq = LinearEqConstraints(EQ_A,EQ_B)
le = LinearLeConstraints(LE_A, LE_B)
obj = QuadraticObjective(OBJ_A, OBJ_B)
_vars = ["x_%s" % i for i in range(4)]
x = torch.ones(4).double()
print("obj:", obj(x))
return Problem(_vars, obj, le, eq)