adds tests for parameters

Author: Transurgeon

cvxpy/reductions/solvers/nlp_solvers/diff_engine/c_problem.py

@@ -45,6 +45,12 @@ def __init__(self, cvxpy_problem: cp.Problem, verbose: bool = True):
         if ctx.param_dict:
             _diffengine.problem_register_params(
                 self._capsule, list(ctx.param_dict.values()))
+            # Set initial parameter values
+            theta = np.concatenate([
+                np.asarray(p.value, dtype=np.float64).flatten(order='F')
+                for p in cvxpy_problem.parameters()
+            ])
+            _diffengine.problem_update_params(self._capsule, theta)
 
     def update_params(self, theta: np.ndarray) -> None:
         """Update parameter values in the C DAG.

cvxpy/tests/nlp_tests/test_nlp_parameters.py

@@ -0,0 +1,71 @@
+"""
+Copyright, the CVXPY authors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import numpy as np
+import pytest
+
+import cvxpy as cp
+from cvxpy.reductions.solvers.defines import INSTALLED_SOLVERS
+
+pytestmark = pytest.mark.skipif(
+    'IPOPT' not in INSTALLED_SOLVERS,
+    reason="IPOPT is not installed")
+
+
+class TestNLPParameters:
+
+    def test_scalar_parameter(self):
+        """min p * x^2 + x, analytical solution: val = -1/(4p)."""
+        x = cp.Variable()
+        p = cp.Parameter(value=2.0)
+        prob = cp.Problem(cp.Minimize(p * x**2 + x), [x >= -5])
+
+        prob.solve(nlp=True, solver='IPOPT')
+        assert np.isclose(prob.value, -1.0 / (4 * 2.0), atol=1e-4)
+
+        p.value = 4.0
+        prob.solve(nlp=True, solver='IPOPT')
+        assert np.isclose(prob.value, -1.0 / (4 * 4.0), atol=1e-4)
+
+    def test_vector_parameter(self):
+        """min p @ x with simplex constraint."""
+        x = cp.Variable(2)
+        p = cp.Parameter(2, value=[1.0, 2.0])
+        prob = cp.Problem(cp.Minimize(p @ x), [x >= 0, cp.sum(x) == 1])
+
+        prob.solve(nlp=True, solver='IPOPT')
+        assert np.isclose(prob.value, 1.0, atol=1e-4)
+        assert np.allclose(x.value, [1.0, 0.0], atol=1e-3)
+
+        p.value = [3.0, 1.0]
+        prob.solve(nlp=True, solver='IPOPT')
+        assert np.isclose(prob.value, 1.0, atol=1e-4)
+        assert np.allclose(x.value, [0.0, 1.0], atol=1e-3)
+
+    def test_matrix_parameter(self):
+        """min ||A @ x - b||^2 with parametric A."""
+        A = cp.Parameter((2, 2), value=np.eye(2))
+        x = cp.Variable(2)
+        b = np.array([1.0, 2.0])
+        prob = cp.Problem(
+            cp.Minimize(cp.sum_squares(A @ x - b)),
+            [x >= -10, x <= 10])
+
+        prob.solve(nlp=True, solver='IPOPT')
+        assert np.allclose(x.value, [1.0, 2.0], atol=1e-3)
+
+        A.value = np.array([[0.0, 1.0], [1.0, 0.0]])
+        prob.solve(nlp=True, solver='IPOPT')
+        assert np.allclose(x.value, [2.0, 1.0], atol=1e-3)