From 6bf4b90c90f15f4ab60833bddf5b5756d1a6b1f6 Mon Sep 17 00:00:00 2001 From: Elizabeth Alexander Hunt Date: Thu, 2 Jul 2026 11:55:17 -0700 Subject: Init --- Homework/math4610/test/eigen.t.c | 147 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 147 insertions(+) create mode 100644 Homework/math4610/test/eigen.t.c (limited to 'Homework/math4610/test/eigen.t.c') diff --git a/Homework/math4610/test/eigen.t.c b/Homework/math4610/test/eigen.t.c new file mode 100644 index 0000000..dc01aa7 --- /dev/null +++ b/Homework/math4610/test/eigen.t.c @@ -0,0 +1,147 @@ +#include "lizfcm.test.h" +#include + +Matrix_double *eigen_test_matrix() { + // produces a matrix that has eigenvalues [5 + sqrt{17}, 2, 5 - sqrt{17}] + Matrix_double *m = InitMatrixWithSize(double, 3, 3, 0.0); + m->data[0]->data[0] = 2.0; + m->data[0]->data[1] = 2.0; + m->data[0]->data[2] = 4.0; + m->data[1]->data[0] = 1.0; + m->data[1]->data[1] = 4.0; + m->data[1]->data[2] = 7.0; + m->data[2]->data[1] = 2.0; + m->data[2]->data[2] = 6.0; + return m; +} + +UTEST(eigen, least_dominant_eigenvalue) { + Matrix_double *m = eigen_test_matrix(); + + double expected_least_dominant_eigenvalue = 0.87689; // 5 - sqrt(17) + double tolerance = 0.0001; + uint64_t max_iterations = 64; + + Array_double *v_guess = InitArrayWithSize(double, 3, 1.0); + double approx_least_dominant_eigenvalue = + least_dominant_eigenvalue(m, v_guess, tolerance, max_iterations); + + EXPECT_NEAR(expected_least_dominant_eigenvalue, + approx_least_dominant_eigenvalue, tolerance); +} + +UTEST(eigen, dominant_eigenvalue) { + Matrix_double *m = InitMatrixWithSize(double, 2, 2, 0.0); + m->data[0]->data[0] = 2.0; + m->data[0]->data[1] = -12.0; + m->data[1]->data[0] = 1.0; + m->data[1]->data[1] = -5.0; + + Array_double *v_guess = InitArrayWithSize(double, 2, 1.0); + double tolerance = 0.0001; + uint64_t max_iterations = 64; + + double expect_dominant_eigenvalue = -2.0; + + double approx_dominant_eigenvalue = + dominant_eigenvalue(m, v_guess, tolerance, max_iterations); + + EXPECT_NEAR(expect_dominant_eigenvalue, approx_dominant_eigenvalue, + tolerance); + free_matrix(m); + free_vector(v_guess); +} + +UTEST(eigen, shifted_eigenvalue) { + Matrix_double *m = eigen_test_matrix(); + + double least_dominant_eigenvalue = 0.87689; // 5 - sqrt{17} + double dominant_eigenvalue = 9.12311; // 5 + sqrt{17} + double expected_middle_eigenvalue = 2.0; + double shift = (dominant_eigenvalue + least_dominant_eigenvalue) / 2.0; + + double tolerance = 0.0001; + uint64_t max_iterations = 64; + Array_double *v_guess = InitArray(double, {0.5, 1.0, 0.75}); + + double approx_middle_eigenvalue = shift_inverse_power_eigenvalue( + m, v_guess, shift, tolerance, max_iterations); + + EXPECT_NEAR(approx_middle_eigenvalue, expected_middle_eigenvalue, tolerance); +} + +UTEST(eigen, partition_find_eigenvalues) { + Matrix_double *m = eigen_test_matrix(); + + double least_dominant_eigenvalue = 0.87689; // 5 - sqrt{17} + double dominant_eigenvalue = 9.12311; // 5 + sqrt{17} + double expected_middle_eigenvalue = 2.0; + double expected_eigenvalues[3] = {least_dominant_eigenvalue, + expected_middle_eigenvalue, + dominant_eigenvalue}; + + size_t partitions = 10; + Matrix_double *guesses = InitMatrixWithSize(double, partitions, 3, 0.0); + for (size_t y = 0; y < guesses->rows; y++) { + free_vector(guesses->data[y]); + guesses->data[y] = InitArray(double, {0.5, 1.0, 0.75}); + } + + double tolerance = 0.0001; + uint64_t max_iterations = 64; + + int eigenvalues_found[3] = {false, false, false}; + Array_double *partition_eigenvalues = + partition_find_eigenvalues(m, guesses, tolerance, max_iterations); + + for (size_t i = 0; i < partition_eigenvalues->size; i++) + for (size_t eigenvalue_i = 0; eigenvalue_i < 3; eigenvalue_i++) + if (fabs(partition_eigenvalues->data[i] - expected_eigenvalues[i]) <= + tolerance) + eigenvalues_found[eigenvalue_i] = true; + + for (size_t eigenvalue_i = 0; eigenvalue_i < 3; eigenvalue_i++) + EXPECT_TRUE(eigenvalues_found[eigenvalue_i]); +} + +UTEST(eigen, leslie_matrix_dominant_eigenvalue) { + Array_double *felicity = InitArray(double, {0.0, 1.5, 0.8}); + Array_double *survivor_ratios = InitArray(double, {0.8, 0.55}); + Matrix_double *leslie = leslie_matrix(survivor_ratios, felicity); + Array_double *v_guess = InitArrayWithSize(double, 3, 2.0); + double tolerance = 0.0001; + uint64_t max_iterations = 64; + + double expect_dominant_eigenvalue = 1.22005; + + double approx_dominant_eigenvalue = + dominant_eigenvalue(leslie, v_guess, tolerance, max_iterations); + + EXPECT_NEAR(expect_dominant_eigenvalue, approx_dominant_eigenvalue, + tolerance); + + free_vector(v_guess); + free_vector(survivor_ratios); + free_vector(felicity); + free_matrix(leslie); +} +UTEST(eigen, leslie_matrix) { + Array_double *felicity = InitArray(double, {0.0, 1.5, 0.8}); + Array_double *survivor_ratios = InitArray(double, {0.8, 0.55}); + + Matrix_double *m = InitMatrixWithSize(double, 3, 3, 0.0); + m->data[0]->data[0] = 0.0; + m->data[0]->data[1] = 1.5; + m->data[0]->data[2] = 0.8; + m->data[1]->data[0] = 0.8; + m->data[2]->data[1] = 0.55; + + Matrix_double *leslie = leslie_matrix(survivor_ratios, felicity); + + EXPECT_TRUE(matrix_equal(leslie, m)); + + free_matrix(leslie); + free_matrix(m); + free_vector(felicity); + free_vector(survivor_ratios); +} -- cgit v1.3