def test_polish_random(self):
# Random QP problem
sp.random.seed(6)
self.n = 30
self.m = 50
Pt = sparse.random(self.n, self.n)
self.P = Pt.T @ Pt
self.q = np.random.randn(self.n)
self.A = sparse.csc_matrix(np.random.randn(self.m, self.n))
self.l = -3 + np.random.randn(self.m)
self.u = 3 + np.random.randn(self.m)
self.model = osqp.OSQP()
self.model.setup(P=self.P,
q=self.q,
A=self.A,
l=self.l,
u=self.u,
**self.opts)
# Solve problem
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P, self.q, self.A,
self.l, self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(res.info.obj_val,
obj_sol,
decimal=decimal_tol)
def test_polish_unconstrained(self):
# Unconstrained QP problem
sp.random.seed(4)
self.n = 30
self.m = 0
P = sparse.diags(np.random.rand(self.n)) + 0.2 * sparse.eye(self.n)
self.P = P.tocsc()
self.q = np.random.randn(self.n)
self.A = sparse.csc_matrix((self.m, self.n))
self.l = np.array([])
self.u = np.array([])
self.model = osqp.OSQP()
self.model.setup(P=self.P,
q=self.q,
A=self.A,
l=self.l,
u=self.u,
**self.opts)
# Solve problem
res = self.model.solve()
x_sol, _, obj_sol = solve_high_accuracy(self.P, self.q, self.A, self.l,
self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(res.info.obj_val,
obj_sol,
decimal=decimal_tol)
def test_polish_simple(self):
# Simple QP problem
self.P = sparse.diags([11., 0.], format='csc')
self.q = np.array([3, 4])
self.A = sparse.csc_matrix([[-1, 0], [0, -1], [-1, -3], [2, 5], [3,
4]])
self.u = np.array([0, 0, -15, 100, 80])
self.l = -1e05 * np.ones(len(self.u))
self.n = self.P.shape[0]
self.m = self.A.shape[0]
self.model = osqp.OSQP()
self.model.setup(P=self.P,
q=self.q,
A=self.A,
l=self.l,
u=self.u,
**self.opts)
# Solve problem
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P, self.q, self.A,
self.l, self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(res.info.obj_val,
obj_sol,
decimal=decimal_tol)
def test_basic_QP(self):
# Solve problem
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P, self.q, self.A,
self.l, self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(
res.info.obj_val, obj_sol, decimal=decimal_tol)
def test_update_P_allind(self):
# Update matrix P
Px = self.P_triu_new.data
self.model.update(Px=Px)
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P_new, self.q, self.A,
self.l, self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(res.info.obj_val,
obj_sol,
decimal=decimal_tol)
def test_update_u(self):
# Update lower bound
u_new = 1000 * np.ones(self.m)
self.model.update(u=u_new)
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P, self.q, self.A,
self.l, u_new)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(
res.info.obj_val, obj_sol, decimal=decimal_tol)
def test_update_q(self):
# Update linear cost
q_new = np.array([10, 20])
self.model.update(q=q_new)
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P, q_new, self.A,
self.l, self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(
res.info.obj_val, obj_sol, decimal=decimal_tol)
def test_update_A(self):
# Update matrix A
Ax = self.A_new.data
Ax_idx = np.arange(self.A_new.nnz)
self.model.update(Ax=Ax, Ax_idx=Ax_idx)
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P, self.q, self.A_new,
self.l, self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(res.info.obj_val,
obj_sol,
decimal=decimal_tol)
def test_update_P_A_indP(self):
# Update matrices P and A
Px = self.P_triu_new.data
Px_idx = np.arange(self.P_triu_new.nnz)
Ax = self.A_new.data
self.model.update(Px=Px, Px_idx=Px_idx, Ax=Ax)
res = self.model.solve()
x_sol, y_sol, obj_sol = solve_high_accuracy(self.P_new, self.q,
self.A_new, self.l, self.u)
# Assert close
nptest.assert_allclose(res.x, x_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_allclose(res.y, y_sol, rtol=rel_tol, atol=abs_tol)
nptest.assert_almost_equal(res.info.obj_val,
obj_sol,
decimal=decimal_tol)