Unit tests

src/ncopt/funs.py

@@ -10,7 +10,7 @@ class f_rosenbrock:
     Nonsmooth Rosenbrock function (see 5.1 in Curtis, Overton
     "SQP FOR NONSMOOTH CONSTRAINED OPTIMIZATION")
 
-    x -> w|x_1^2 − x_2| + (1 − x_1)^2
+    x -> w|x_1^2 - x_2| + (1 - x_1)^2
     """
 
     def __init__(self, w=8):

src/ncopt/sqpgs/main.py

@@ -487,6 +487,7 @@ def solve(self):
         )
 
         self.status = qp["status"]
+        self.objective_val = qp["primal objective"]
         self.cvx_sol_x = np.array(qp["x"]).squeeze()
 
         self.d = self.cvx_sol_x[: self.dim]

tests/test_rosenbrock.py

@@ -19,20 +19,17 @@ def test_rosenbrock_from_zero():
     x_k = problem.solve()
     np.testing.assert_array_almost_equal(x_k, xstar, decimal=4)
 
-    return
-
 
 def test_rosenbrock_from_rand():
     gI = [g]
     gE = []
     xstar = np.array([1 / np.sqrt(2), 0.5])
-    x0 = np.random.rand(2)
+    rng = np.random.default_rng(0)
+    x0 = rng.random(2)
     problem = SQPGS(f, gI, gE, x0=x0, tol=1e-8, max_iter=200, verbose=False)
     x_k = problem.solve()
     np.testing.assert_array_almost_equal(x_k, xstar, decimal=4)
 
-    return
-
 
 def test_rosenbrock_with_eq():
     g1 = g_linear(A=np.eye(2), b=np.ones(2))
@@ -42,5 +39,3 @@ def test_rosenbrock_with_eq():
     problem = SQPGS(f, gI, gE, tol=1e-8, max_iter=200, verbose=False)
     x_k = problem.solve()
     np.testing.assert_array_almost_equal(x_k, xstar, decimal=4)
-
-    return

tests/test_subproblem.py

@@ -0,0 +1,64 @@
+import numpy as np
+import pytest
+
+from ncopt.sqpgs.main import SubproblemSQPGS
+
+
+@pytest.fixture
+def subproblem_ineq() -> SubproblemSQPGS:
+    dim = 2
+    p0 = 2
+    pI = np.array([3])
+    pE = np.array([], dtype=int)
+    assert_tol = 1e-5
+    subproblem = SubproblemSQPGS(dim, p0, pI, pE, assert_tol)
+    D_f = np.array([[-2.0, 1.0], [-2.04236205, -1.0], [-1.92172864, -1.0]])
+    D_gI = [np.array([[0.0, 2.0], [0.0, 2.0], [1.41421356, 0.0], [1.41421356, 0.0]])]
+    subproblem.update(
+        H=np.eye(2, dtype=float),
+        rho=0.1,
+        D_f=D_f,
+        D_gI=D_gI,
+        D_gE=[],
+        f_k=1.0,
+        gI_k=np.array([-1.0]),
+        gE_k=np.array([], dtype=float),
+    )
+    return subproblem
+
+
+def test_subproblem_ineq(subproblem_ineq: SubproblemSQPGS):
+    subproblem_ineq.solve()
+    assert subproblem_ineq.status == "optimal"
+    assert np.isclose(subproblem_ineq.objective_val, 0.080804459)
+
+
+@pytest.fixture
+def subproblem_eq() -> SubproblemSQPGS:
+    dim = 2
+    p0 = 2
+    pI = np.array([], dtype=int)
+    pE = np.array([4, 4])
+    assert_tol = 1e-5
+    subproblem = SubproblemSQPGS(dim, p0, pI, pE, assert_tol)
+    D_gE = [
+        np.array([[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [1.0, 0.0]]),
+        np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]),
+    ]
+    subproblem.update(
+        H=np.eye(2, dtype=float),
+        rho=0.1,
+        D_f=np.array([-2.0, 1.0]),
+        D_gI=[],
+        D_gE=D_gE,
+        f_k=1.0,
+        gI_k=np.array([], dtype=float),
+        gE_k=np.array([-1.0, -1.0]),
+    )
+    return subproblem
+
+
+def test_subproblem_eq(subproblem_eq: SubproblemSQPGS):
+    subproblem_eq.solve()
+    assert subproblem_eq.status == "optimal"
+    assert np.isclose(subproblem_eq.objective_val, 0.99500000)