diff --git a/homeworks/hw-8.org b/homeworks/hw-8.org index f4f4ebd..92a380f 100644 --- a/homeworks/hw-8.org +++ b/homeworks/hw-8.org @@ -4,11 +4,9 @@ #+LATEX: \setlength\parindent{0pt} #+OPTIONS: toc:nil -TODO: Update LIZFCM org file with jacobi solve - * Question One See ~UTEST(jacobi, solve_jacobi)~ in ~test/jacobi.t.c~ and the entry -~Jacobi -> solve_jacobi~ in the LIZFCM API documentation. +~Jacobi / Gauss-Siedel -> solve_jacobi~ in the LIZFCM API documentation. * Question Two We cannot just perform the Jacobi algorithm on a Leslie matrix since it is obviously not diagonally dominant - which is a requirement. It is @@ -24,14 +22,290 @@ direct solution method. It's simply the nature of the Jacobi algorithm being a convergent solution that determines its accuracy. LU factorization also performs in order $O(n^3)$ runtime for an $n \times n$ -matrix, whereas the Jacobi algorithm runs in order $O(k n^2) = O(n^2)$ but with the -con that $k$ is given by the convergence criteria, which might end up worse in -some cases, than LU. +matrix, whereas the Jacobi algorithm runs in order $O(k n^2) = O(n^2)$ on average +but with the con that $k$ is given by some function on both the convergence criteria and the number of +nonzero entries in the matrix - which might end up worse in some cases than the LU decomp approach. * Question Three See ~UTEST(jacobi, gauss_siedel_solve)~ in ~test/jacobi.t.c~ which runs the same unit test as ~UTEST(jacobi, solve_jacobi)~ but using the -~Jacobi -> gauss_siedel_solve~ method as documented in the LIZFCM API reference. +~Jacobi / Gauss-Siedel -> gauss_siedel_solve~ method as documented in the LIZFCM API reference. * Question Four, Five +We produce the following operation counts (by hackily adding the operation count as the last element +to the solution vector) and errors - the sum of each vector elements' absolute value away from 1.0 +using the proceeding patch and unit test. +| N | JAC opr | JAC err | GS opr | GS err | LU opr | LU err | +| 5 | 1622 | 0.001244 | 577 | 0.000098 | 430 | 0.000000 | +| 6 | 2812 | 0.001205 | 775 | 0.000080 | 681 | 0.000000 | +| 7 | 5396 | 0.001187 | 860 | 0.000178 | 1015 | 0.000000 | +| 8 | 5618 | 0.001468 | 1255 | 0.000121 | 1444 | 0.000000 | +| 9 | 7534 | 0.001638 | 1754 | 0.000091 | 1980 | 0.000000 | +| 10 | 10342 | 0.001425 | 1847 | 0.000435 | 2635 | 0.000000 | +| 11 | 12870 | 0.001595 | 2185 | 0.000368 | 3421 | 0.000000 | +| 12 | 17511 | 0.001860 | 2912 | 0.000322 | 4350 | 0.000000 | +| 13 | 16226 | 0.001631 | 3362 | 0.000270 | 5434 | 0.000000 | +| 14 | 34333 | 0.001976 | 3844 | 0.000121 | 6685 | 0.000000 | +| 15 | 38474 | 0.001922 | 4358 | 0.000311 | 8115 | 0.000000 | +| 16 | 40405 | 0.002061 | 4904 | 0.000204 | 9736 | 0.000000 | +| 17 | 58518 | 0.002125 | 5482 | 0.000311 | 11560 | 0.000000 | +| 18 | 68079 | 0.002114 | 6092 | 0.000279 | 13599 | 0.000000 | +| 19 | 95802 | 0.002159 | 6734 | 0.000335 | 15865 | 0.000000 | +| 20 | 85696 | 0.002141 | 7408 | 0.000289 | 18370 | 0.000000 | +| 21 | 89026 | 0.002316 | 8114 | 0.000393 | 21126 | 0.000000 | +| 22 | 101537 | 0.002344 | 8852 | 0.000414 | 24145 | 0.000000 | +| 23 | 148040 | 0.002323 | 9622 | 0.000230 | 27439 | 0.000000 | +| 24 | 137605 | 0.002348 | 10424 | 0.000213 | 31020 | 0.000000 | +| 25 | 169374 | 0.002409 | 11258 | 0.000894 | 34900 | 0.000000 | +| 26 | 215166 | 0.002502 | 12124 | 0.000564 | 39091 | 0.000000 | +| 27 | 175476 | 0.002616 | 13022 | 0.000535 | 43605 | 0.000000 | +| 28 | 268454 | 0.002651 | 13952 | 0.000690 | 48454 | 0.000000 | +| 29 | 267034 | 0.002697 | 14914 | 0.000675 | 53650 | 0.000000 | +| 30 | 277193 | 0.002686 | 15908 | 0.000542 | 59205 | 0.000000 | +| 31 | 336792 | 0.002736 | 16934 | 0.000390 | 65131 | 0.000000 | +| 32 | 293958 | 0.002741 | 17992 | 0.000660 | 71440 | 0.000000 | +| 33 | 323638 | 0.002893 | 19082 | 0.001072 | 78144 | 0.000000 | +| 34 | 375104 | 0.003001 | 20204 | 0.001018 | 85255 | 0.000000 | +| 35 | 436092 | 0.003004 | 21358 | 0.000912 | 92785 | 0.000000 | +| 36 | 538143 | 0.003005 | 22544 | 0.000954 | 100746 | 0.000000 | +| 37 | 511886 | 0.003029 | 23762 | 0.000462 | 109150 | 0.000000 | +| 38 | 551332 | 0.003070 | 25012 | 0.000996 | 118009 | 0.000000 | +| 39 | 592750 | 0.003110 | 26294 | 0.000989 | 127335 | 0.000000 | +| 40 | 704208 | 0.003165 | 27608 | 0.000583 | 137140 | 0.000000 | + +#+BEGIN_SRC +diff --git a/src/matrix.c b/src/matrix.c +index 901a426..af5529f 100644 +--- a/src/matrix.c ++++ b/src/matrix.c +@@ -144,20 +144,54 @@ Array_double *solve_matrix_lu_bsubst(Matrix_double *m, Array_double *b) { + assert(b->size == m->rows); + assert(m->rows == m->cols); + ++ double opr = 0; ++ ++ opr += b->size; + Array_double *x = copy_vector(b); ++ ++ size_t n = m->rows; ++ opr += n * n; // (u copy) ++ opr += n * n; // l_empty ++ opr += n * n + n; // copy + put_identity_diagonal ++ opr += n; // pivot check ++ opr += m->cols; ++ for (size_t x = 0; x < m->cols; x++) { ++ opr += (m->rows - (x + 1)); ++ for (size_t y = x + 1; y < m->rows; y++) { ++ opr += 1; ++ opr += 2; // -factor ++ opr += 4 * n; // scale, add_v, free_vector ++ opr += 1; // -factor ++ } ++ } ++ opr += n; + Matrix_double **u_l = lu_decomp(m); ++ + Matrix_double *u = u_l[0]; + Matrix_double *l = u_l[1]; + ++ opr += n; ++ for (int64_t row = n - 1; row >= 0; row--) { ++ opr += 2 * (n - row); ++ opr += 1; ++ } + Array_double *b_fsub = fsubst(l, b); ++ ++ opr += n; ++ for (size_t x = 0; x < n; x++) { ++ opr += 2 * (x + 1); ++ opr += 1; // /= l->data[row]->data[row] ++ } + x = bsubst(u, b_fsub); +- free_vector(b_fsub); + ++ free_vector(b_fsub); + free_matrix(u); + free_matrix(l); + free(u_l); + +- return x; ++ Array_double *copy = add_element(x, opr); ++ free_vector(x); ++ return copy; + } + + Matrix_double *gaussian_elimination(Matrix_double *m) { +@@ -231,18 +265,36 @@ Array_double *jacobi_solve(Matrix_double *m, Array_double *b, + assert(b->size == m->cols); + size_t iter = max_iterations; + ++ double opr = 0; ++ ++ opr += 2 * b->size; // to initialize two vectors with the same dim of b twice + Array_double *x_k = InitArrayWithSize(double, b->size, 0.0); + Array_double *x_k_1 = + InitArrayWithSize(double, b->size, rand_from(0.1, 10.0)); + ++ // add since these wouldn't be accounter for after the loop ++ opr += 1; // iter decrement ++ opr += ++ 3 * x_k_1->size; // 1 to perform x_k_1, x_k and 2 to perform ||x_k_1||_2 + while ((--iter) > 0 && l2_distance(x_k_1, x_k) > l2_convergence_tolerance) { ++ opr += 1; // iter decrement ++ opr += ++ 3 * x_k_1->size; // 1 to perform x_k_1, x_k and 2 to perform ||x_k_1||_2 ++ ++ opr += m->rows; // row for add oprs + for (size_t i = 0; i < m->rows; i++) { + double delta = 0.0; ++ ++ opr += m->cols; + for (size_t j = 0; j < m->cols; j++) { + if (i == j) + continue; ++ ++ opr += 1; + delta += m->data[i]->data[j] * x_k->data[j]; + } ++ ++ opr += 2; + x_k_1->data[i] = (b->data[i] - delta) / m->data[i]->data[i]; + } + +@@ -251,8 +303,9 @@ Array_double *jacobi_solve(Matrix_double *m, Array_double *b, + x_k_1 = tmp; + } + +- free_vector(x_k); +- return x_k_1; ++ Array_double *copy = add_element(x_k_1, opr); ++ free_vector(x_k_1); ++ return copy; + } + + Array_double *gauss_siedel_solve(Matrix_double *m, Array_double *b, +@@ -262,30 +315,48 @@ Array_double *gauss_siedel_solve(Matrix_double *m, Array_double *b, + assert(b->size == m->cols); + size_t iter = max_iterations; + ++ double opr = 0; ++ ++ opr += 2 * b->size; // to initialize two vectors with the same dim of b twice + Array_double *x_k = InitArrayWithSize(double, b->size, 0.0); + Array_double *x_k_1 = + InitArrayWithSize(double, b->size, rand_from(0.1, 10.0)); + + while ((--iter) > 0) { ++ opr += 1; // iter decrement ++ ++ opr += x_k->size; // copy oprs + for (size_t i = 0; i < x_k->size; i++) + x_k->data[i] = x_k_1->data[i]; + ++ opr += m->rows; // row for add oprs + for (size_t i = 0; i < m->rows; i++) { + double delta = 0.0; ++ ++ opr += m->cols; + for (size_t j = 0; j < m->cols; j++) { + if (i == j) + continue; ++ ++ opr += 1; + delta += m->data[i]->data[j] * x_k_1->data[j]; + } ++ ++ opr += 2; + x_k_1->data[i] = (b->data[i] - delta) / m->data[i]->data[i]; + } + ++ opr += ++ 3 * x_k_1->size; // 1 to perform x_k_1, x_k and 2 to perform ||x_k_1||_2 + if (l2_distance(x_k_1, x_k) <= l2_convergence_tolerance) + break; + } + + free_vector(x_k); +- return x_k_1; ++ ++ Array_double *copy = add_element(x_k_1, opr); ++ free_vector(x_k_1); ++ return copy; + } +#+END_SRC + + +And this unit test: +#+BEGIN_SRC c +UTEST(hw_8, p4_5) { + printf("| N | JAC opr | JAC err | GS opr | GS err | LU opr | LU err | \n"); + + for (size_t i = 5; i < 100; i++) { + Matrix_double *m = generate_ddm(i); + double oprs[3] = {0.0, 0.0, 0.0}; + double errs[3] = {0.0, 0.0, 0.0}; + + Array_double *b_1 = InitArrayWithSize(double, m->rows, 1.0); + Array_double *b = m_dot_v(m, b_1); + double tolerance = 0.001; + size_t max_iter = 400; + + // JACOBI + { + Array_double *solution_with_opr_count = + jacobi_solve(m, b, tolerance, max_iter); + Array_double *solution = slice_element(solution_with_opr_count, + solution_with_opr_count->size - 1); + + for (size_t i = 0; i < solution->size; i++) + errs[0] += fabs(solution->data[i] - 1.0); + + oprs[0] = + solution_with_opr_count->data[solution_with_opr_count->size - 1]; + + free_vector(solution); + free_vector(solution_with_opr_count); + } + + // GAUSS-SIEDEL + { + Array_double *solution_with_opr_count = + gauss_siedel_solve(m, b, tolerance, max_iter); + Array_double *solution = slice_element(solution_with_opr_count, + solution_with_opr_count->size - 1); + + for (size_t i = 0; i < solution->size; i++) + errs[1] += fabs(solution->data[i] - 1.0); + + oprs[1] = + solution_with_opr_count->data[solution_with_opr_count->size - 1]; + + free_vector(solution); + free_vector(solution_with_opr_count); + } + + // LU-BSUBST + { + Array_double *solution_with_opr_count = solve_matrix_lu_bsubst(m, b); + Array_double *solution = slice_element(solution_with_opr_count, + solution_with_opr_count->size - 1); + + for (size_t i = 0; i < solution->size; i++) + errs[2] += fabs(solution->data[i] - 1.0); + + oprs[2] = + solution_with_opr_count->data[solution_with_opr_count->size - 1]; + + free_vector(solution); + free_vector(solution_with_opr_count); + } + free_matrix(m); + free_vector(b_1); + free_vector(b); + + printf("| %zu | %f | %f | %f | %f | %f | %f | \n", i, oprs[0], errs[0], + oprs[1], errs[1], oprs[2], errs[2]); + } +} +#+END_SRC diff --git a/homeworks/hw-8.pdf b/homeworks/hw-8.pdf new file mode 100644 index 0000000..601a75b Binary files /dev/null and b/homeworks/hw-8.pdf differ diff --git a/homeworks/hw-8.tex b/homeworks/hw-8.tex new file mode 100644 index 0000000..5074689 --- /dev/null +++ b/homeworks/hw-8.tex @@ -0,0 +1,344 @@ +% Created 2023-12-09 Sat 21:43 +% Intended LaTeX compiler: pdflatex +\documentclass[11pt]{article} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{graphicx} +\usepackage{longtable} +\usepackage{wrapfig} +\usepackage{rotating} +\usepackage[normalem]{ulem} +\usepackage{amsmath} +\usepackage{amssymb} +\usepackage{capt-of} +\usepackage{hyperref} +\notindent \notag \usepackage{amsmath} \usepackage[a4paper,margin=1in,portrait]{geometry} +\author{Elizabeth Hunt} +\date{\today} +\title{Homework 7} +\hypersetup{ + pdfauthor={Elizabeth Hunt}, + pdftitle={Homework 7}, + pdfkeywords={}, + pdfsubject={}, + pdfcreator={Emacs 28.2 (Org mode 9.7-pre)}, + pdflang={English}} +\begin{document} + +\maketitle +\setlength\parindent{0pt} + +\section{Question One} +\label{sec:orgb6d5cda} +See \texttt{UTEST(jacobi, solve\_jacobi)} in \texttt{test/jacobi.t.c} and the entry +\texttt{Jacobi / Gauss-Siedel -> solve\_jacobi} in the LIZFCM API documentation. +\section{Question Two} +\label{sec:org9786314} +We cannot just perform the Jacobi algorithm on a Leslie matrix since +it is obviously not diagonally dominant - which is a requirement. It is +certainly not always the case, but, if a Leslie matrix \(L\) is invertible, we can +first perform gaussian elimination on \(L\) augmented with \(n_{k+1}\) +to obtain \(n_k\) with the Jacobi method. See \texttt{UTEST(jacobi, leslie\_solve)} +in \texttt{test/jacobi.t.c} for an example wherein this method is tested on a Leslie +matrix to recompute a given initial population distribution. + +In terms of accuracy, an LU factorization and back substitution approach will +always be as correct as possible within the limits of computation; it's a +direct solution method. It's simply the nature of the Jacobi algorithm being +a convergent solution that determines its accuracy. + +LU factorization also performs in order \(O(n^3)\) runtime for an \(n \times n\) +matrix, whereas the Jacobi algorithm runs in order \(O(k n^2) = O(n^2)\) on average +but with the con that \(k\) is given by some function on both the convergence criteria and the number of +nonzero entries in the matrix - which might end up worse in some cases than the LU decomp approach. + +\section{Question Three} +\label{sec:org0ea87d0} +See \texttt{UTEST(jacobi, gauss\_siedel\_solve)} in \texttt{test/jacobi.t.c} which runs the same +unit test as \texttt{UTEST(jacobi, solve\_jacobi)} but using the +\texttt{Jacobi / Gauss-Siedel -> gauss\_siedel\_solve} method as documented in the LIZFCM API reference. + +\section{Question Four, Five} +\label{sec:org8eea2ae} +We produce the following operation counts (by hackily adding the operation count as the last element +to the solution vector) and errors - the sum of each vector elements' absolute value away from 1.0 +using the proceeding patch and unit test. + +\begin{center} +\begin{tabular}{rrrrrrr} +N & JAC opr & JAC err & GS opr & GS err & LU opr & LU err\\[0pt] +5 & 1622 & 0.001244 & 577 & 0.000098 & 430 & 0.000000\\[0pt] +6 & 2812 & 0.001205 & 775 & 0.000080 & 681 & 0.000000\\[0pt] +7 & 5396 & 0.001187 & 860 & 0.000178 & 1015 & 0.000000\\[0pt] +8 & 5618 & 0.001468 & 1255 & 0.000121 & 1444 & 0.000000\\[0pt] +9 & 7534 & 0.001638 & 1754 & 0.000091 & 1980 & 0.000000\\[0pt] +10 & 10342 & 0.001425 & 1847 & 0.000435 & 2635 & 0.000000\\[0pt] +11 & 12870 & 0.001595 & 2185 & 0.000368 & 3421 & 0.000000\\[0pt] +12 & 17511 & 0.001860 & 2912 & 0.000322 & 4350 & 0.000000\\[0pt] +13 & 16226 & 0.001631 & 3362 & 0.000270 & 5434 & 0.000000\\[0pt] +14 & 34333 & 0.001976 & 3844 & 0.000121 & 6685 & 0.000000\\[0pt] +15 & 38474 & 0.001922 & 4358 & 0.000311 & 8115 & 0.000000\\[0pt] +16 & 40405 & 0.002061 & 4904 & 0.000204 & 9736 & 0.000000\\[0pt] +17 & 58518 & 0.002125 & 5482 & 0.000311 & 11560 & 0.000000\\[0pt] +18 & 68079 & 0.002114 & 6092 & 0.000279 & 13599 & 0.000000\\[0pt] +19 & 95802 & 0.002159 & 6734 & 0.000335 & 15865 & 0.000000\\[0pt] +20 & 85696 & 0.002141 & 7408 & 0.000289 & 18370 & 0.000000\\[0pt] +21 & 89026 & 0.002316 & 8114 & 0.000393 & 21126 & 0.000000\\[0pt] +22 & 101537 & 0.002344 & 8852 & 0.000414 & 24145 & 0.000000\\[0pt] +23 & 148040 & 0.002323 & 9622 & 0.000230 & 27439 & 0.000000\\[0pt] +24 & 137605 & 0.002348 & 10424 & 0.000213 & 31020 & 0.000000\\[0pt] +25 & 169374 & 0.002409 & 11258 & 0.000894 & 34900 & 0.000000\\[0pt] +26 & 215166 & 0.002502 & 12124 & 0.000564 & 39091 & 0.000000\\[0pt] +27 & 175476 & 0.002616 & 13022 & 0.000535 & 43605 & 0.000000\\[0pt] +28 & 268454 & 0.002651 & 13952 & 0.000690 & 48454 & 0.000000\\[0pt] +29 & 267034 & 0.002697 & 14914 & 0.000675 & 53650 & 0.000000\\[0pt] +30 & 277193 & 0.002686 & 15908 & 0.000542 & 59205 & 0.000000\\[0pt] +31 & 336792 & 0.002736 & 16934 & 0.000390 & 65131 & 0.000000\\[0pt] +32 & 293958 & 0.002741 & 17992 & 0.000660 & 71440 & 0.000000\\[0pt] +33 & 323638 & 0.002893 & 19082 & 0.001072 & 78144 & 0.000000\\[0pt] +34 & 375104 & 0.003001 & 20204 & 0.001018 & 85255 & 0.000000\\[0pt] +35 & 436092 & 0.003004 & 21358 & 0.000912 & 92785 & 0.000000\\[0pt] +36 & 538143 & 0.003005 & 22544 & 0.000954 & 100746 & 0.000000\\[0pt] +37 & 511886 & 0.003029 & 23762 & 0.000462 & 109150 & 0.000000\\[0pt] +38 & 551332 & 0.003070 & 25012 & 0.000996 & 118009 & 0.000000\\[0pt] +39 & 592750 & 0.003110 & 26294 & 0.000989 & 127335 & 0.000000\\[0pt] +40 & 704208 & 0.003165 & 27608 & 0.000583 & 137140 & 0.000000\\[0pt] +\end{tabular} +\end{center} + +\begin{verbatim} +diff --git a/src/matrix.c b/src/matrix.c +index 901a426..af5529f 100644 +--- a/src/matrix.c ++++ b/src/matrix.c +@@ -144,20 +144,54 @@ Array_double *solve_matrix_lu_bsubst(Matrix_double *m, Array_double *b) { + assert(b->size == m->rows); + assert(m->rows == m->cols); + ++ double opr = 0; ++ ++ opr += b->size; + Array_double *x = copy_vector(b); ++ ++ size_t n = m->rows; ++ opr += n * n; // (u copy) ++ opr += n * n; // l_empty ++ opr += n * n + n; // copy + put_identity_diagonal ++ opr += n; // pivot check ++ opr += m->cols; ++ for (size_t x = 0; x < m->cols; x++) { ++ opr += (m->rows - (x + 1)); ++ for (size_t y = x + 1; y < m->rows; y++) { ++ opr += 1; ++ opr += 2; // -factor ++ opr += 4 * n; // scale, add_v, free_vector ++ opr += 1; // -factor ++ } ++ } ++ opr += n; + Matrix_double **u_l = lu_decomp(m); ++ + Matrix_double *u = u_l[0]; + Matrix_double *l = u_l[1]; + ++ opr += n; ++ for (int64_t row = n - 1; row >= 0; row--) { ++ opr += 2 * (n - row); ++ opr += 1; ++ } + Array_double *b_fsub = fsubst(l, b); ++ ++ opr += n; ++ for (size_t x = 0; x < n; x++) { ++ opr += 2 * (x + 1); ++ opr += 1; // /= l->data[row]->data[row] ++ } + x = bsubst(u, b_fsub); +- free_vector(b_fsub); + ++ free_vector(b_fsub); + free_matrix(u); + free_matrix(l); + free(u_l); + +- return x; ++ Array_double *copy = add_element(x, opr); ++ free_vector(x); ++ return copy; + } + + Matrix_double *gaussian_elimination(Matrix_double *m) { +@@ -231,18 +265,36 @@ Array_double *jacobi_solve(Matrix_double *m, Array_double *b, + assert(b->size == m->cols); + size_t iter = max_iterations; + ++ double opr = 0; ++ ++ opr += 2 * b->size; // to initialize two vectors with the same dim of b twice + Array_double *x_k = InitArrayWithSize(double, b->size, 0.0); + Array_double *x_k_1 = + InitArrayWithSize(double, b->size, rand_from(0.1, 10.0)); + ++ // add since these wouldn't be accounter for after the loop ++ opr += 1; // iter decrement ++ opr += ++ 3 * x_k_1->size; // 1 to perform x_k_1, x_k and 2 to perform ||x_k_1||_2 + while ((--iter) > 0 && l2_distance(x_k_1, x_k) > l2_convergence_tolerance) { ++ opr += 1; // iter decrement ++ opr += ++ 3 * x_k_1->size; // 1 to perform x_k_1, x_k and 2 to perform ||x_k_1||_2 ++ ++ opr += m->rows; // row for add oprs + for (size_t i = 0; i < m->rows; i++) { + double delta = 0.0; ++ ++ opr += m->cols; + for (size_t j = 0; j < m->cols; j++) { + if (i == j) + continue; ++ ++ opr += 1; + delta += m->data[i]->data[j] * x_k->data[j]; + } ++ ++ opr += 2; + x_k_1->data[i] = (b->data[i] - delta) / m->data[i]->data[i]; + } + +@@ -251,8 +303,9 @@ Array_double *jacobi_solve(Matrix_double *m, Array_double *b, + x_k_1 = tmp; + } + +- free_vector(x_k); +- return x_k_1; ++ Array_double *copy = add_element(x_k_1, opr); ++ free_vector(x_k_1); ++ return copy; + } + + Array_double *gauss_siedel_solve(Matrix_double *m, Array_double *b, +@@ -262,30 +315,48 @@ Array_double *gauss_siedel_solve(Matrix_double *m, Array_double *b, + assert(b->size == m->cols); + size_t iter = max_iterations; + ++ double opr = 0; ++ ++ opr += 2 * b->size; // to initialize two vectors with the same dim of b twice + Array_double *x_k = InitArrayWithSize(double, b->size, 0.0); + Array_double *x_k_1 = + InitArrayWithSize(double, b->size, rand_from(0.1, 10.0)); + + while ((--iter) > 0) { ++ opr += 1; // iter decrement ++ ++ opr += x_k->size; // copy oprs + for (size_t i = 0; i < x_k->size; i++) + x_k->data[i] = x_k_1->data[i]; + ++ opr += m->rows; // row for add oprs + for (size_t i = 0; i < m->rows; i++) { + double delta = 0.0; ++ ++ opr += m->cols; + for (size_t j = 0; j < m->cols; j++) { + if (i == j) + continue; ++ ++ opr += 1; + delta += m->data[i]->data[j] * x_k_1->data[j]; + } ++ ++ opr += 2; + x_k_1->data[i] = (b->data[i] - delta) / m->data[i]->data[i]; + } + ++ opr += ++ 3 * x_k_1->size; // 1 to perform x_k_1, x_k and 2 to perform ||x_k_1||_2 + if (l2_distance(x_k_1, x_k) <= l2_convergence_tolerance) + break; + } + + free_vector(x_k); +- return x_k_1; ++ ++ Array_double *copy = add_element(x_k_1, opr); ++ free_vector(x_k_1); ++ return copy; + } +\end{verbatim} + + +And this unit test: +\begin{verbatim} +UTEST(hw_8, p4_5) { + printf("| N | JAC opr | JAC err | GS opr | GS err | LU opr | LU err | \n"); + + for (size_t i = 5; i < 100; i++) { + Matrix_double *m = generate_ddm(i); + double oprs[3] = {0.0, 0.0, 0.0}; + double errs[3] = {0.0, 0.0, 0.0}; + + Array_double *b_1 = InitArrayWithSize(double, m->rows, 1.0); + Array_double *b = m_dot_v(m, b_1); + double tolerance = 0.001; + size_t max_iter = 400; + + // JACOBI + { + Array_double *solution_with_opr_count = + jacobi_solve(m, b, tolerance, max_iter); + Array_double *solution = slice_element(solution_with_opr_count, + solution_with_opr_count->size - 1); + + for (size_t i = 0; i < solution->size; i++) + errs[0] += fabs(solution->data[i] - 1.0); + + oprs[0] = + solution_with_opr_count->data[solution_with_opr_count->size - 1]; + + free_vector(solution); + free_vector(solution_with_opr_count); + } + + // GAUSS-SIEDEL + { + Array_double *solution_with_opr_count = + gauss_siedel_solve(m, b, tolerance, max_iter); + Array_double *solution = slice_element(solution_with_opr_count, + solution_with_opr_count->size - 1); + + for (size_t i = 0; i < solution->size; i++) + errs[1] += fabs(solution->data[i] - 1.0); + + oprs[1] = + solution_with_opr_count->data[solution_with_opr_count->size - 1]; + + free_vector(solution); + free_vector(solution_with_opr_count); + } + + // LU-BSUBST + { + Array_double *solution_with_opr_count = solve_matrix_lu_bsubst(m, b); + Array_double *solution = slice_element(solution_with_opr_count, + solution_with_opr_count->size - 1); + + for (size_t i = 0; i < solution->size; i++) + errs[2] += fabs(solution->data[i] - 1.0); + + oprs[2] = + solution_with_opr_count->data[solution_with_opr_count->size - 1]; + + free_vector(solution); + free_vector(solution_with_opr_count); + } + free_matrix(m); + free_vector(b_1); + free_vector(b); + + printf("| %zu | %f | %f | %f | %f | %f | %f | \n", i, oprs[0], errs[0], + oprs[1], errs[1], oprs[2], errs[2]); + } +} +\end{verbatim} +\end{document} \ No newline at end of file