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-rw-r--r--experiments/pivoting_vs_no_pivoting.cpp196
1 files changed, 0 insertions, 196 deletions
diff --git a/experiments/pivoting_vs_no_pivoting.cpp b/experiments/pivoting_vs_no_pivoting.cpp
deleted file mode 100644
index 2f043cb..0000000
--- a/experiments/pivoting_vs_no_pivoting.cpp
+++ /dev/null
@@ -1,196 +0,0 @@
-import linalgebra;
-import std;
-
-using linalgebra::Matrix;
-using linalgebra::Vector;
-
-struct NoPivotLU {
- Matrix L;
- Matrix U;
- bool failed = false;
- std::size_t fail_step = 0;
-};
-
-NoPivotLU lu_no_pivot(const Matrix& A, double tol = 1e-14) {
- const std::size_t n = A.rows();
- Matrix work = A;
- Matrix L = Matrix::zeros(n, n);
- for (std::size_t i = 0; i < n; ++i) L(i, i) = 1.0;
- Matrix U = Matrix::zeros(n, n);
-
- for (std::size_t k = 0; k < n; ++k) {
- if (std::abs(work(k, k)) <= tol) {
- return NoPivotLU{std::move(L), std::move(U), true, k};
- }
- for (std::size_t j = k; j < n; ++j) U(k, j) = work(k, j);
- for (std::size_t i = k + 1; i < n; ++i) {
- L(i, k) = work(i, k) / work(k, k);
- for (std::size_t j = k + 1; j < n; ++j) {
- work(i, j) -= L(i, k) * work(k, j);
- }
- }
- }
- return NoPivotLU{std::move(L), std::move(U), false, 0};
-}
-
-std::optional<Vector> solve_no_pivot(const NoPivotLU& f, const Vector& b) {
- if (f.failed) return std::nullopt;
- try {
- const Vector y = linalgebra::forward_substitution(f.L, b, 1e-14, /*unit_diagonal=*/true);
- return linalgebra::backward_substitution(f.U, y);
- } catch (...) {
- return std::nullopt;
- }
-}
-
-double solve_residual(const Matrix& A, const Vector& x, const Vector& b) {
- return linalgebra::norm2(A * x - b);
-}
-
-double reconstruction_error(const Matrix& A, const linalgebra::LUResult& lu) {
- const std::size_t n = A.rows();
- Matrix PA(n, n);
- for (std::size_t i = 0; i < n; ++i)
- for (std::size_t j = 0; j < n; ++j)
- PA(i, j) = A(lu.perm[i], j);
- const Matrix LU_prod = lu.L * lu.U;
- double err = 0.0;
- for (std::size_t i = 0; i < n; ++i)
- for (std::size_t j = 0; j < n; ++j) {
- const double d = PA(i, j) - LU_prod(i, j);
- err += d * d;
- }
- return std::sqrt(err);
-}
-
-void print_header(const std::string& title) {
- std::cout << "\n" << std::string(60, '=') << "\n";
- std::cout << " " << title << "\n";
- std::cout << std::string(60, '=') << "\n";
- std::cout << std::left
- << std::setw(22) << "Method"
- << std::setw(20) << "||Ax - b||"
- << std::setw(20) << "||PA - LU||"
- << "\n";
- std::cout << std::string(60, '-') << "\n";
-}
-
-void report_pivoted(const Matrix& A, const Vector& b) {
- try {
- const linalgebra::LUResult lu = linalgebra::lu_factor(A);
- const Vector x = linalgebra::lu_solve(lu, b);
- std::cout << std::left << std::setw(22) << "Pivoted LU"
- << std::setw(20) << std::scientific << std::setprecision(3)
- << solve_residual(A, x, b)
- << std::setw(20) << reconstruction_error(A, lu)
- << "\n";
- } catch (const std::exception& e) {
- std::cout << std::left << std::setw(22) << "Pivoted LU"
- << "FAILED: " << e.what() << "\n";
- }
-}
-
-void report_no_pivot(const Matrix& A, const Vector& b) {
- const NoPivotLU f = lu_no_pivot(A);
- if (f.failed) {
- std::cout << std::left << std::setw(22) << "No-pivot LU"
- << "FAILED at step " << f.fail_step << " (zero pivot)\n";
- return;
- }
- const auto x_opt = solve_no_pivot(f, b);
- if (!x_opt) {
- std::cout << std::left << std::setw(22) << "No-pivot LU"
- << "FAILED during solve (singular U)\n";
- return;
- }
- const Matrix LU_prod = f.L * f.U;
- double rec_err = 0.0;
- for (std::size_t i = 0; i < A.rows(); ++i)
- for (std::size_t j = 0; j < A.cols(); ++j) {
- const double d = A(i, j) - LU_prod(i, j);
- rec_err += d * d;
- }
- rec_err = std::sqrt(rec_err);
-
- std::cout << std::left << std::setw(22) << "No-pivot LU"
- << std::setw(20) << std::scientific << std::setprecision(3)
- << solve_residual(A, *x_opt, b)
- << std::setw(20) << rec_err
- << "\n";
-}
-
-void run_case(const std::string& label, const Matrix& A, const Vector& b) {
- print_header(label);
- report_pivoted(A, b);
- report_no_pivot(A, b);
-}
-
-void exp_random(std::size_t n = 8) {
- std::mt19937 rng(42);
- std::uniform_real_distribution<double> dist(-5.0, 5.0);
- Matrix A(n, n);
- for (std::size_t i = 0; i < n; ++i)
- for (std::size_t j = 0; j < n; ++j)
- A(i, j) = dist(rng);
-
- Vector b(n);
- for (std::size_t i = 0; i < n; ++i) b[i] = dist(rng);
-
- run_case("Random 8x8 (well-conditioned)", A, b);
-}
-
-void exp_badly_scaled() {
- const Matrix A{
- {1e-14, 1.0, 2.0 },
- {1.0, 3.0, 4.0 },
- {2.0, 5.0, 7.0 }
- };
- const Vector b{1e-14 + 3.0, 8.0, 14.0};
- run_case("Badly scaled (row norms differ by 10^14)", A, b);
-}
-
-void exp_epsilon_pathology() {
- constexpr double eps = 1e-15;
- const Matrix A{{eps, 1.0}, {1.0, 2.0}};
- const Vector b{1.0 + eps, 3.0};
- run_case("Epsilon pathology [[1e-15,1],[1,2]] (classic)", A, b);
- std::cout << " Note: exact solution is x = [1, 1]\n";
-}
-
-void exp_amplified_multiplier() {
- const Matrix A{
- {0.001, 1.0, 0.0, 0.0 },
- {1.0, 2.0, 1.0, 0.0 },
- {0.0, 1.0, 3.0, 1.0 },
- {0.0, 0.0, 1.0, 4.0 }
- };
- const Vector b = A * Vector{1.0, 2.0, 3.0, 4.0};
- run_case("Amplified multiplier (small (1,1) pivot, 4x4)", A, b);
- std::cout << " Note: exact solution is x = [1, 2, 3, 4]\n";
-}
-
-void exp_permutation() {
- const Matrix A{
- {0.0, 0.0, 3.0},
- {0.0, 2.0, 1.0},
- {5.0, 1.0, 0.0}
- };
- const Vector b = A * Vector{1.0, -1.0, 2.0};
- run_case("Multiple row swaps required (zeros in pivot positions)", A, b);
-}
-
-int main() {
- std::cout << std::string(60, '*') << "\n";
- std::cout << " Pivoting vs No-Pivoting LU Experiment\n";
- std::cout << std::string(60, '*') << "\n";
- std::cout << "Residual = ||Ax - b||_2 (solve accuracy)\n";
- std::cout << "Recon err = ||PA - LU||_F (factorization accuracy)\n";
-
- exp_random();
- exp_badly_scaled();
- exp_epsilon_pathology();
- exp_amplified_multiplier();
- exp_permutation();
-
- return 0;
-}