skip 3 tests on uno

Author: dance858

cvxpy/tests/nlp_tests/test_nlp_solvers.py

@@ -360,46 +360,46 @@ def test_circle_packing_formulation_one(self, solver):
         checker = DerivativeChecker(problem)
         checker.run_and_assert()
 
-    # comment this out for now because UNo computes a different point
-    #def test_circle_packing_formulation_two(self, solver):
-    #    """Using norm_inf. This test revealed a very subtle bug in the unpacking of
-    #    the ipopt solution. Some variables were mistakenly reordered. It was fixed
-    #    in https://github.com/cvxgrp/cvxpy-ipopt/pull/82"""
-    #    rng = np.random.default_rng(5)
-    #    n = 3
-    #    radius = rng.uniform(1.0, 3.0, n)
-#
-    #    centers = cp.Variable((2, n), name='c')
-    #    constraints = []
-    #    for i in range(n - 1):
-    #        for j in range(i + 1, n):
-    #            constraints += [cp.sum(cp.square(centers[:, i] - centers[:, j])) >=
-    #                            (radius[i] + radius[j]) ** 2]
-#
-    #    centers.value = rng.uniform(-5.0, 5.0, (2, n))
-    #    obj = cp.Minimize(cp.max(cp.norm_inf(centers, axis=0) + radius))
-    #    prob = cp.Problem(obj, constraints)
-    #    prob.solve(solver=solver, nlp=True)
-#
-    #    assert np.allclose(obj.value, 4.602738956101437)
-#
-    #    residuals = []
-    #    for i in range(n - 1):
-    #        for j in range(i + 1, n):
-    #            dist_sq = np.linalg.norm(centers.value[:, i] - centers.value[:, j]) ** 2
-    #            min_dist_sq = (radius[i] + radius[j]) ** 2
-    #            residuals.append(dist_sq - min_dist_sq)
-    #
-    #    assert(np.all(np.array(residuals) <= 1e-6))
-#
-    #    # Ipopt finds these centers, but Knitro rotates them (but finds the same
-    #    # objective value)
-    #    #true_sol = np.array([[1.73655994, -1.98685738, 2.57208783],
-    #    #                     [1.99273311, -1.67415425, -2.57208783]])
-    #    #assert np.allclose(centers.value, true_sol)
-#
-    #    checker = DerivativeChecker(prob)
-    #    checker.run_and_assert()
+    @pytest.mark.skipif('UNO' in INSTALLED_SOLVERS, reason='UNO finds a KKT point with worse obj')
+    def test_circle_packing_formulation_two(self, solver):
+        """Using norm_inf. This test revealed a very subtle bug in the unpacking of
+        the ipopt solution. Some variables were mistakenly reordered. It was fixed
+        in https://github.com/cvxgrp/cvxpy-ipopt/pull/82"""
+        rng = np.random.default_rng(5)
+        n = 3
+        radius = rng.uniform(1.0, 3.0, n)
+
+        centers = cp.Variable((2, n), name='c')
+        constraints = []
+        for i in range(n - 1):
+            for j in range(i + 1, n):
+                constraints += [cp.sum(cp.square(centers[:, i] - centers[:, j])) >=
+                                (radius[i] + radius[j]) ** 2]
+
+        centers.value = rng.uniform(-5.0, 5.0, (2, n))
+        obj = cp.Minimize(cp.max(cp.norm_inf(centers, axis=0) + radius))
+        prob = cp.Problem(obj, constraints)
+        prob.solve(solver=solver, nlp=True)
+
+        assert np.allclose(obj.value, 4.602738956101437)
+
+        residuals = []
+        for i in range(n - 1):
+            for j in range(i + 1, n):
+                dist_sq = np.linalg.norm(centers.value[:, i] - centers.value[:, j]) ** 2
+                min_dist_sq = (radius[i] + radius[j]) ** 2
+                residuals.append(dist_sq - min_dist_sq)
+    
+        assert(np.all(np.array(residuals) <= 1e-6))
+
+        # Ipopt finds these centers, but Knitro rotates them (but finds the same
+        # objective value)
+        #true_sol = np.array([[1.73655994, -1.98685738, 2.57208783],
+        #                     [1.99273311, -1.67415425, -2.57208783]])
+        #assert np.allclose(centers.value, true_sol)
+
+        checker = DerivativeChecker(prob)
+        checker.run_and_assert()
 
     def test_circle_packing_formulation_three(self, solver):
         """Using max max abs."""
@@ -428,31 +428,31 @@ def test_circle_packing_formulation_three(self, solver):
         checker = DerivativeChecker(prob)
         checker.run_and_assert()
 
-    # temporarily comment this out as uno fails
-    #def test_geo_mean(self, solver):
-    #    x = cp.Variable(3, nonneg=True)
-    #    geo_mean = cp.geo_mean(x)
-    #    objective = cp.Maximize(geo_mean)
-    #    constraints = [cp.sum(x) == 1]
-    #    problem = cp.Problem(objective, constraints)
-    #    problem.solve(solver=solver, nlp=True)
-    #    assert problem.status == cp.OPTIMAL
-    #    assert np.allclose(x.value, np.array([1/3, 1/3, 1/3]))
+    @pytest.mark.skipif('UNO' in INSTALLED_SOLVERS, reason='UNO reaches iteration limit')
+    def test_geo_mean(self, solver):
+        x = cp.Variable(3, nonneg=True)
+        geo_mean = cp.geo_mean(x)
+        objective = cp.Maximize(geo_mean)
+        constraints = [cp.sum(x) == 1]
+        problem = cp.Problem(objective, constraints)
+        problem.solve(solver=solver, nlp=True)
+        assert problem.status == cp.OPTIMAL
+        assert np.allclose(x.value, np.array([1/3, 1/3, 1/3]))
 
-    #    checker = DerivativeChecker(problem)
-    #    checker.run_and_assert()
+        checker = DerivativeChecker(problem)
+        checker.run_and_assert()
 
-    # temporarily comment this out as uno fails
-    #def test_geo_mean2(self, solver):
-    #    p = np.array([.07, .12, .23, .19, .39])
-    #    x = cp.Variable(5, nonneg=True)
-    #    prob = cp.Problem(cp.Maximize(cp.geo_mean(x, p)), [cp.sum(x) <= 1])
-    #    prob.solve(solver=solver, nlp=True)
-    #    x_true = p/sum(p)
-    #    assert prob.status == cp.OPTIMAL
-    #    assert np.allclose(x.value, x_true)
-    #    checker = DerivativeChecker(prob)
-    #    checker.run_and_assert()
+    @pytest.mark.skipif('UNO' in INSTALLED_SOLVERS, reason='UNO reaches iteration limit')
+    def test_geo_mean2(self, solver):
+        p = np.array([.07, .12, .23, .19, .39])
+        x = cp.Variable(5, nonneg=True)
+        prob = cp.Problem(cp.Maximize(cp.geo_mean(x, p)), [cp.sum(x) <= 1])
+        prob.solve(solver=solver, nlp=True)
+        x_true = p/sum(p)
+        assert prob.status == cp.OPTIMAL
+        assert np.allclose(x.value, x_true)
+        checker = DerivativeChecker(prob)
+        checker.run_and_assert()
 
     def test_div_composition(self, solver):
         x = cp.Variable(nonneg=True, bounds=[1, 5])