Fix projected_gradient_method

src/deepcpp/algorithm.cpp

@@ -112,7 +112,7 @@ VectorXd projected_gradient_method(
     double tolerance)
 {
     double step_size = 1 / mat.norm();
-    VectorXd x_old = projection(target);
+    VectorXd x_old = projection(initial_guess);
     for (int i = 0; i < max_iterations; ++i)
     {
         VectorXd x_new = projection(x_old - step_size * (mat * x_old - target));

tests/deepc/test_math.py

@@ -1,6 +1,6 @@
 import unittest
 import numpy as np
-from deepc.math import left_pseudoinverse, right_pseudoinverse, hankel_matrix
+from deepc.math import left_pseudoinverse, right_pseudoinverse, hankel_matrix, projected_gradient_method
 
 
 class TestLeftPseudoinverse(unittest.TestCase):
@@ -76,6 +76,17 @@ def test_size_3_and_2_rows(self):
     def test_size_3_and_3_rows(self):
         np.testing.assert_array_equal(hankel_matrix(3, [(1, 2), (3, 4), (5, 6)]), [[1], [2], [3], [4], [5], [6]])
 
+class TestProjectedGradientMethod(unittest.TestCase):
+    def test_1x1(self):
+        mat = np.array([[1, 0], [0, 1]])
+        x_ini = np.array([1, 1])
+        target = np.array([0, 0])
+        def constrain(x):
+            return x
+
+        res = projected_gradient_method(mat, x_ini, target, constrain)
+
+        np.testing.assert_array_almost_equal(res, np.array([0, 0]), decimal=5)
 
 if __name__ == "__main__":
     unittest.main()

tests/deepcpp/test_algorithm.cpp

@@ -171,7 +171,7 @@ TEST(HankelMatrix_2D_data, Size_3_and_3_row)
 	EXPECT_EQ(res, expected);
 }
 
-TEST(Algorithm, ProjectedGradientMethod)
+TEST(Algorithm, projected_gradient_method)
 {
 	auto mat = Matrix({1, 0}, {0, 1});
 	auto initial_guess = Vector(1, 1);
@@ -180,5 +180,5 @@ TEST(Algorithm, ProjectedGradientMethod)
 
 	auto res = projected_gradient_method(mat, initial_guess, target, projection);
 
-	EXPECT_EQ(res, Vector(0, 0));
+	EXPECT_LE(res.norm(), 1e-6);
 }