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Elizabeth Hunt 2023-12-11 19:24:33 -07:00
parent 9b73fc2978
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GPG Key ID: 52B3774857EB24B1
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@ -1,4 +1,4 @@
% Created 2023-11-29 Wed 18:33
% Created 2023-12-11 Mon 19:22
% Intended LaTeX compiler: pdflatex
\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
@ -15,13 +15,13 @@
\notindent \notag \usepackage{amsmath} \usepackage[a4paper,margin=1in,portrait]{geometry}
\author{Elizabeth Hunt}
\date{\today}
\title{LIZFCM Software Manual (v0.5)}
\title{LIZFCM Software Manual (v0.6)}
\hypersetup{
pdfauthor={Elizabeth Hunt},
pdftitle={LIZFCM Software Manual (v0.5)},
pdftitle={LIZFCM Software Manual (v0.6)},
pdfkeywords={},
pdfsubject={},
pdfcreator={Emacs 29.1 (Org mode 9.7-pre)},
pdfcreator={Emacs 28.2 (Org mode 9.7-pre)},
pdflang={English}}
\begin{document}
@ -29,8 +29,9 @@
\tableofcontents
\setlength\parindent{0pt}
\section{Design}
\label{sec:orge6394d6}
\label{sec:org63deaf6}
The LIZFCM static library (at \url{https://github.com/Simponic/math-4610}) is a successor to my
attempt at writing codes for the Fundamentals of Computational Mathematics course in Common
Lisp, but the effort required to meet the requirement of creating a static library became
@ -46,8 +47,9 @@ the C programming language. I have a couple tenets for its design:
\item Routines are separated into "modules" that follow a form of separation of concerns
in files, and not individual files per function.
\end{itemize}
\section{Compilation}
\label{sec:org19eb4ad}
\label{sec:org7291327}
A provided \texttt{Makefile} is added for convencience. It has been tested on an \texttt{arm}-based M1 machine running
MacOS as well as \texttt{x86} Arch Linux.
@ -71,12 +73,13 @@ produce an object file:
Which is then bundled into a static library in \texttt{lib/lizfcm.a} and can be linked
in the standard method.
\section{The LIZFCM API}
\label{sec:orgcdad892}
\label{sec:org1ebe7fa}
\subsection{Simple Routines}
\label{sec:org414c16a}
\label{sec:orgff18c6b}
\subsubsection{\texttt{smaceps}}
\label{sec:org1f871c1}
\label{sec:org443df5e}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{smaceps}
@ -100,8 +103,9 @@ float smaceps() {
return machine_epsilon;
}
\end{verbatim}
\subsubsection{\texttt{dmaceps}}
\label{sec:orga6517f5}
\label{sec:org5121603}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{dmaceps}
@ -125,10 +129,11 @@ double dmaceps() {
return machine_epsilon;
}
\end{verbatim}
\subsection{Derivative Routines}
\label{sec:org7037c95}
\label{sec:org6fd324c}
\subsubsection{\texttt{central\_derivative\_at}}
\label{sec:org5004cc1}
\label{sec:orge9f0821}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{central\_derivative\_at}
@ -157,8 +162,9 @@ double central_derivative_at(double (*f)(double), double a, double h) {
return (y2 - y1) / (x2 - x1);
}
\end{verbatim}
\subsubsection{\texttt{forward\_derivative\_at}}
\label{sec:orgfe0e436}
\label{sec:org8720f28}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{forward\_derivative\_at}
@ -187,8 +193,9 @@ double forward_derivative_at(double (*f)(double), double a, double h) {
return (y2 - y1) / (x2 - x1);
}
\end{verbatim}
\subsubsection{\texttt{backward\_derivative\_at}}
\label{sec:orgf50d7e6}
\label{sec:org1589b19}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{backward\_derivative\_at}
@ -217,10 +224,11 @@ double backward_derivative_at(double (*f)(double), double a, double h) {
return (y2 - y1) / (x2 - x1);
}
\end{verbatim}
\subsection{Vector Routines}
\label{sec:org125771d}
\label{sec:org493841e}
\subsubsection{Vector Arithmetic: \texttt{add\_v, minus\_v}}
\label{sec:orgf07f7dd}
\label{sec:org3912c29}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name(s): \texttt{add\_v}, \texttt{minus\_v}
@ -249,8 +257,9 @@ Array_double *minus_v(Array_double *v1, Array_double *v2) {
return sub;
}
\end{verbatim}
\subsubsection{Norms: \texttt{l1\_norm}, \texttt{l2\_norm}, \texttt{linf\_norm}}
\label{sec:org3f50ecb}
\label{sec:orged74cfb}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name(s): \texttt{l1\_norm}, \texttt{l2\_norm}, \texttt{linf\_norm}
@ -282,8 +291,9 @@ double linf_norm(Array_double *v) {
return max;
}
\end{verbatim}
\subsubsection{\texttt{vector\_distance}}
\label{sec:org77ad0f5}
\label{sec:org20a5773}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{vector\_distance}
@ -302,8 +312,9 @@ double vector_distance(Array_double *v1, Array_double *v2,
return dist;
}
\end{verbatim}
\subsubsection{Distances: \texttt{l1\_distance}, \texttt{l2\_distance}, \texttt{linf\_distance}}
\label{sec:org83b7946}
\label{sec:orgac16178}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name(s): \texttt{l1\_distance}, \texttt{l2\_distance}, \texttt{linf\_distance}
@ -327,8 +338,9 @@ double linf_distance(Array_double *v1, Array_double *v2) {
return vector_distance(v1, v2, &linf_norm);
}
\end{verbatim}
\subsubsection{\texttt{sum\_v}}
\label{sec:orgb0e87d6}
\label{sec:org876aafa}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{sum\_v}
@ -345,8 +357,9 @@ double sum_v(Array_double *v) {
return sum;
}
\end{verbatim}
\subsubsection{\texttt{scale\_v}}
\label{sec:org08dbec0}
\label{sec:orgf1d236c}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{scale\_v}
@ -363,8 +376,9 @@ Array_double *scale_v(Array_double *v, double m) {
return copy;
}
\end{verbatim}
\subsubsection{\texttt{free\_vector}}
\label{sec:org36eb399}
\label{sec:org2ca163d}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{free\_vector}
@ -380,8 +394,9 @@ void free_vector(Array_double *v) {
free(v);
}
\end{verbatim}
\subsubsection{\texttt{add\_element}}
\label{sec:org987fa04}
\label{sec:org7a99233}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{add\_element}
@ -399,8 +414,9 @@ Array_double *add_element(Array_double *v, double x) {
return pushed;
}
\end{verbatim}
\subsubsection{\texttt{slice\_element}}
\label{sec:orged034ec}
\label{sec:org6c07c99}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{slice\_element}
@ -417,8 +433,9 @@ Array_double *slice_element(Array_double *v, size_t x) {
return sliced;
}
\end{verbatim}
\subsubsection{\texttt{copy\_vector}}
\label{sec:org3d6d716}
\label{sec:org81f7cc1}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{copy\_vector}
@ -436,8 +453,9 @@ Array_double *copy_vector(Array_double *v) {
return copy;
}
\end{verbatim}
\subsubsection{\texttt{format\_vector\_into}}
\label{sec:orgb4ab2db}
\label{sec:orgd168171}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{format\_vector\_into}
@ -465,10 +483,11 @@ void format_vector_into(Array_double *v, char *s) {
strcat(s, "\n");
}
\end{verbatim}
\subsection{Matrix Routines}
\label{sec:orgd984ad2}
\label{sec:org5c45c12}
\subsubsection{\texttt{lu\_decomp}}
\label{sec:org184e384}
\label{sec:orgf1e0ac3}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{lu\_decomp}
@ -528,7 +547,7 @@ Matrix_double **lu_decomp(Matrix_double *m) {
}
\end{verbatim}
\subsubsection{\texttt{bsubst}}
\label{sec:org78c1bb9}
\label{sec:orgec7e4b5}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{bsubst}
@ -553,7 +572,7 @@ Array_double *bsubst(Matrix_double *u, Array_double *b) {
}
\end{verbatim}
\subsubsection{\texttt{fsubst}}
\label{sec:org9d7af79}
\label{sec:org72ff2ed}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{fsubst}
@ -579,8 +598,9 @@ Array_double *fsubst(Matrix_double *l, Array_double *b) {
return x;
}
\end{verbatim}
\subsubsection{\texttt{solve\_matrix\_lu\_bsubst}}
\label{sec:org85d9237}
\label{sec:orga735557}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -615,8 +635,9 @@ Array_double *solve_matrix_lu_bsubst(Matrix_double *m, Array_double *b) {
return x;
}
\end{verbatim}
\subsubsection{\texttt{gaussian\_elimination}}
\label{sec:orgd188f79}
\label{sec:org71d2519}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -629,30 +650,32 @@ applying reduction to all other rows. The general idea is available at \url{http
\begin{verbatim}
Matrix_double *gaussian_elimination(Matrix_double *m) {
uint64_t h = 0;
uint64_t k = 0;
uint64_t h = 0, k = 0;
Matrix_double *m_cp = copy_matrix(m);
while (h < m_cp->rows && k < m_cp->cols) {
uint64_t max_row = 0;
double total_max = 0.0;
uint64_t max_row = h;
double max_val = 0.0;
for (uint64_t row = h; row < m_cp->rows; row++) {
double this_max = c_max(fabs(m_cp->data[row]->data[k]), total_max);
if (c_max(this_max, total_max) == this_max) {
double val = fabs(m_cp->data[row]->data[k]);
if (val > max_val) {
max_val = val;
max_row = row;
}
}
if (max_row == 0) {
if (max_val == 0.0) {
k++;
continue;
}
Array_double *swp = m_cp->data[max_row];
m_cp->data[max_row] = m_cp->data[h];
m_cp->data[h] = swp;
if (max_row != h) {
Array_double *swp = m_cp->data[max_row];
m_cp->data[max_row] = m_cp->data[h];
m_cp->data[h] = swp;
}
for (uint64_t row = h + 1; row < m_cp->rows; row++) {
double factor = m_cp->data[row]->data[k] / m_cp->data[h]->data[k];
@ -670,8 +693,9 @@ Matrix_double *gaussian_elimination(Matrix_double *m) {
return m_cp;
}
\end{verbatim}
\subsubsection{\texttt{solve\_matrix\_gaussian}}
\label{sec:org2f14966}
\label{sec:org230915f}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -703,8 +727,10 @@ Array_double *solve_matrix_gaussian(Matrix_double *m, Array_double *b) {
return solution;
}
\end{verbatim}
\subsubsection{\texttt{m\_dot\_v}}
\label{sec:orgc3739fc}
\label{sec:org83c8351}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -724,8 +750,9 @@ Array_double *m_dot_v(Matrix_double *m, Array_double *v) {
return product;
}
\end{verbatim}
\subsubsection{\texttt{put\_identity\_diagonal}}
\label{sec:org6e2dda0}
\label{sec:orge3fcb3e}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -742,8 +769,9 @@ Matrix_double *put_identity_diagonal(Matrix_double *m) {
return copy;
}
\end{verbatim}
\subsubsection{\texttt{slice\_column}}
\label{sec:org6ec00b6}
\label{sec:org95e39ba}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -765,8 +793,9 @@ Matrix_double *slice_column(Matrix_double *m, size_t x) {
return sliced;
}
\end{verbatim}
\subsubsection{\texttt{add\_column}}
\label{sec:org46ea124}
\label{sec:org9a2ad93}
\begin{itemize}
\item Author: Elizabet Hunt
\item Location: \texttt{src/matrix.c}
@ -788,8 +817,9 @@ Matrix_double *add_column(Matrix_double *m, Array_double *v) {
return pushed;
}
\end{verbatim}
\subsubsection{\texttt{copy\_matrix}}
\label{sec:org0fb04f5}
\label{sec:org63765c0}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -807,8 +837,9 @@ Matrix_double *copy_matrix(Matrix_double *m) {
return copy;
}
\end{verbatim}
\subsubsection{\texttt{free\_matrix}}
\label{sec:org1e7bf4e}
\label{sec:orgc337967}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{src/matrix.c}
@ -825,8 +856,9 @@ void free_matrix(Matrix_double *m) {
free(m);
}
\end{verbatim}
\subsubsection{\texttt{format\_matrix\_into}}
\label{sec:org7d927f5}
\label{sec:org6b188b4}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{format\_matrix\_into}
@ -843,7 +875,7 @@ void format_matrix_into(Matrix_double *m, char *s) {
strcpy(s, "empty");
for (size_t y = 0; y < m->rows; ++y) {
char row_s[256];
char row_s[5192];
strcpy(row_s, "");
format_vector_into(m->data[y], row_s);
@ -853,9 +885,9 @@ void format_matrix_into(Matrix_double *m, char *s) {
}
\end{verbatim}
\subsection{Root Finding Methods}
\label{sec:org2d9e027}
\label{sec:org352ccdf}
\subsubsection{\texttt{find\_ivt\_range}}
\label{sec:org13aac0a}
\label{sec:orgb9a0d16}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{find\_ivt\_range}
@ -887,7 +919,7 @@ Array_double *find_ivt_range(double (*f)(double), double start_x, double delta,
}
\end{verbatim}
\subsubsection{\texttt{bisect\_find\_root}}
\label{sec:org37c0d43}
\label{sec:org25382b3}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name(s): \texttt{bisect\_find\_root}
@ -918,7 +950,7 @@ Array_double *bisect_find_root(double (*f)(double), double a, double b,
}
\end{verbatim}
\subsubsection{\texttt{bisect\_find\_root\_with\_error\_assumption}}
\label{sec:orga63019e}
\label{sec:org4b9cb72}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{bisect\_find\_root\_with\_error\_assumption}
@ -945,8 +977,9 @@ double bisect_find_root_with_error_assumption(double (*f)(double), double a,
return root;
}
\end{verbatim}
\subsubsection{\texttt{fixed\_point\_iteration\_method}}
\label{sec:org9325db0}
\label{sec:org4cee2bd}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{fixed\_point\_iteration\_method}
@ -976,8 +1009,9 @@ double fixed_point_iteration_method(double (*f)(double), double (*g)(double),
max_iterations - 1);
}
\end{verbatim}
\subsubsection{\texttt{fixed\_point\_newton\_method}}
\label{sec:orgb571ad9}
\label{sec:org93e3999}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{fixed\_point\_newton\_method}
@ -1004,8 +1038,9 @@ double fixed_point_newton_method(double (*f)(double), double (*fprime)(double),
max_iterations - 1);
}
\end{verbatim}
\subsubsection{\texttt{fixed\_point\_secant\_method}}
\label{sec:org1b33cb8}
\label{sec:orgf3f0711}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{fixed\_point\_secant\_method}
@ -1032,7 +1067,7 @@ double fixed_point_secant_method(double (*f)(double), double x_0, double x_1,
}
\end{verbatim}
\subsubsection{\texttt{fixed\_point\_secant\_bisection\_method}}
\label{sec:org9dad67f}
\label{sec:orgeaef048}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{fixed\_point\_secant\_method}
@ -1082,10 +1117,11 @@ double fixed_point_secant_bisection_method(double (*f)(double), double x_0,
return root;
}
\end{verbatim}
\subsection{Linear Routines}
\label{sec:org89d7d4f}
\label{sec:orge3b6d97}
\subsubsection{\texttt{least\_squares\_lin\_reg}}
\label{sec:orgda34435}
\label{sec:orgcc90c4a}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{least\_squares\_lin\_reg}
@ -1114,10 +1150,11 @@ Line *least_squares_lin_reg(Array_double *x, Array_double *y) {
return line;
}
\end{verbatim}
\subsection{Eigen-Adjacent}
\label{sec:orgc7e86aa}
\label{sec:orga3c637f}
\subsubsection{\texttt{dominant\_eigenvalue}}
\label{sec:org511efa7}
\label{sec:org0306c8a}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{dominant\_eigenvalue}
@ -1161,7 +1198,7 @@ double dominant_eigenvalue(Matrix_double *m, Array_double *v, double tolerance,
}
\end{verbatim}
\subsubsection{\texttt{shift\_inverse\_power\_eigenvalue}}
\label{sec:org2f9dea2}
\label{sec:orgc29637a}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{least\_dominant\_eigenvalue}
@ -1208,8 +1245,9 @@ double shift_inverse_power_eigenvalue(Matrix_double *m, Array_double *v,
return lambda;
}
\end{verbatim}
\subsubsection{\texttt{least\_dominant\_eigenvalue}}
\label{sec:org847cb9b}
\label{sec:org5df73a2}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{least\_dominant\_eigenvalue}
@ -1227,7 +1265,7 @@ double least_dominant_eigenvalue(Matrix_double *m, Array_double *v,
}
\end{verbatim}
\subsubsection{\texttt{partition\_find\_eigenvalues}}
\label{sec:orgb6cc77c}
\label{sec:org3dde7af}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{partition\_find\_eigenvalues}
@ -1268,7 +1306,7 @@ Array_double *partition_find_eigenvalues(Matrix_double *m,
}
\end{verbatim}
\subsubsection{\texttt{leslie\_matrix}}
\label{sec:org2bb3502}
\label{sec:orgca10ed3}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{leslie\_matrix}
@ -1295,13 +1333,128 @@ Matrix_double *leslie_matrix(Array_double *age_class_surivor_ratio,
return leslie;
}
\end{verbatim}
\subsection{Jacobi / Gauss-Siedel}
\label{sec:org91c563c}
\subsubsection{\texttt{jacobi\_solve}}
\label{sec:org2cd6098}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{jacobi\_solve}
\item Location: \texttt{src/matrix.c}
\item Input: a pointer to a diagonally dominant square matrix \(m\), a vector representing
the value \(b\) in \(mx = b\), a double representing the maximum distance between
the solutions produced by iteration \(i\) and \(i+1\) (by L2 norm a.k.a cartesian
distance), and a \texttt{max\_iterations} which we force stop.
\item Output: the converged-upon solution \(x\) to \(mx = b\)
\end{itemize}
\begin{verbatim}
Array_double *jacobi_solve(Matrix_double *m, Array_double *b,
double l2_convergence_tolerance,
size_t max_iterations) {
assert(m->rows == m->cols);
assert(b->size == m->cols);
size_t iter = max_iterations;
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 && l2_distance(x_k_1, x_k) > l2_convergence_tolerance) {
for (size_t i = 0; i < m->rows; i++) {
double delta = 0.0;
for (size_t j = 0; j < m->cols; j++) {
if (i == j)
continue;
delta += m->data[i]->data[j] * x_k->data[j];
}
x_k_1->data[i] = (b->data[i] - delta) / m->data[i]->data[i];
}
Array_double *tmp = x_k;
x_k = x_k_1;
x_k_1 = tmp;
}
free_vector(x_k);
return x_k_1;
}
\end{verbatim}
\subsubsection{\texttt{gauss\_siedel\_solve}}
\label{sec:org6633923}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{gauss\_siedel\_solve}
\item Location: \texttt{src/matrix.c}
\item Input: a pointer to a \href{https://en.wikipedia.org/wiki/Gauss\%E2\%80\%93Seidel\_method}{diagonally dominant or symmetric and positive definite}
square matrix \(m\), a vector representing
the value \(b\) in \(mx = b\), a double representing the maximum distance between
the solutions produced by iteration \(i\) and \(i+1\) (by L2 norm a.k.a cartesian
distance), and a \texttt{max\_iterations} which we force stop.
\item Output: the converged-upon solution \(x\) to \(mx = b\)
\item Description: we use almost the exact same method as \texttt{jacobi\_solve} but modify
only one array in accordance to the Gauss-Siedel method, but which is necessarily
copied before due to the convergence check.
\end{itemize}
\begin{verbatim}
Array_double *gauss_siedel_solve(Matrix_double *m, Array_double *b,
double l2_convergence_tolerance,
size_t max_iterations) {
assert(m->rows == m->cols);
assert(b->size == m->cols);
size_t iter = max_iterations;
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) {
for (size_t i = 0; i < x_k->size; i++)
x_k->data[i] = x_k_1->data[i];
for (size_t i = 0; i < m->rows; i++) {
double delta = 0.0;
for (size_t j = 0; j < m->cols; j++) {
if (i == j)
continue;
delta += m->data[i]->data[j] * x_k_1->data[j];
}
x_k_1->data[i] = (b->data[i] - delta) / m->data[i]->data[i];
}
if (l2_distance(x_k_1, x_k) <= l2_convergence_tolerance)
break;
}
free_vector(x_k);
return x_k_1;
}
\end{verbatim}
\subsection{Appendix / Miscellaneous}
\label{sec:org7ecff9b}
\label{sec:orga72494e}
\subsubsection{Random}
\label{sec:org4940c39}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Name: \texttt{rand\_from}
\item Location: \texttt{src/rand.c}
\item Input: a pair of doubles, min and max to generate a random number min
\(\le\) x \(\le\) max
\item Output: a random double in the constraints shown
\end{itemize}
\begin{verbatim}
double rand_from(double min, double max) {
return min + (rand() / (RAND_MAX / (max - min)));
}
\end{verbatim}
\subsubsection{Data Types}
\label{sec:orgd12d26b}
\label{sec:org8d3f6e1}
\begin{enumerate}
\item \texttt{Line}
\label{sec:org5c75544}
\label{sec:orgc0df901}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{inc/types.h}
@ -1314,7 +1467,7 @@ typedef struct Line {
} Line;
\end{verbatim}
\item The \texttt{Array\_<type>} and \texttt{Matrix\_<type>}
\label{sec:orgc595e92}
\label{sec:org435e816}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{inc/types.h}
@ -1344,11 +1497,12 @@ typedef struct {
} Matrix_int
\end{verbatim}
\end{enumerate}
\subsubsection{Macros}
\label{sec:orgdcaa9de}
\label{sec:orga2161be}
\begin{enumerate}
\item \texttt{c\_max} and \texttt{c\_min}
\label{sec:org8f831d6}
\label{sec:org16ca9c3}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{inc/macros.h}
@ -1360,8 +1514,9 @@ typedef struct {
#define c_max(x, y) (((x) >= (y)) ? (x) : (y))
#define c_min(x, y) (((x) <= (y)) ? (x) : (y))
\end{verbatim}
\item \texttt{InitArray}
\label{sec:org61bb69c}
\label{sec:orgcaff993}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{inc/macros.h}
@ -1380,8 +1535,9 @@ typedef struct {
arr; \
})
\end{verbatim}
\item \texttt{InitArrayWithSize}
\label{sec:org6bc07d2}
\label{sec:orga925ddb}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{inc/macros.h}
@ -1400,8 +1556,9 @@ typedef struct {
arr; \
})
\end{verbatim}
\item \texttt{InitMatrixWithSize}
\label{sec:orgb5de1b7}
\label{sec:orgf90d7c8}
\begin{itemize}
\item Author: Elizabeth Hunt
\item Location: \texttt{inc/macros.h}
@ -1423,4 +1580,4 @@ value
})
\end{verbatim}
\end{enumerate}
\end{document}
\end{document}

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@ -1,4 +1,4 @@
#+TITLE: Homework 7
#+TITLE: Homework 8
#+AUTHOR: Elizabeth Hunt
#+LATEX_HEADER: \notindent \notag \usepackage{amsmath} \usepackage[a4paper,margin=1in,portrait]{geometry}
#+LATEX: \setlength\parindent{0pt}

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@ -1,4 +1,4 @@
% Created 2023-12-09 Sat 21:43
% Created 2023-12-09 Sat 22:06
% Intended LaTeX compiler: pdflatex
\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
@ -15,10 +15,10 @@
\notindent \notag \usepackage{amsmath} \usepackage[a4paper,margin=1in,portrait]{geometry}
\author{Elizabeth Hunt}
\date{\today}
\title{Homework 7}
\title{Homework 8}
\hypersetup{
pdfauthor={Elizabeth Hunt},
pdftitle={Homework 7},
pdftitle={Homework 8},
pdfkeywords={},
pdfsubject={},
pdfcreator={Emacs 28.2 (Org mode 9.7-pre)},
@ -29,11 +29,11 @@
\setlength\parindent{0pt}
\section{Question One}
\label{sec:orgb6d5cda}
\label{sec:org800c743}
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}
\label{sec:org6121bef}
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
@ -53,13 +53,13 @@ but with the con that \(k\) is given by some function on both the convergence cr
nonzero entries in the matrix - which might end up worse in some cases than the LU decomp approach.
\section{Question Three}
\label{sec:org0ea87d0}
\label{sec:org11282e6}
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}
\label{sec:org22b52a9}
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.

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#+TITLE: Homework 9
#+AUTHOR: Elizabeth Hunt
#+LATEX_HEADER: \notindent \notag \usepackage{amsmath} \usepackage[a4paper,margin=1in,portrait]{geometry}
#+LATEX: \setlength\parindent{0pt}
#+OPTIONS: toc:nil
* Question One
With a ~matrix_dimension~ set to 700, I consistently see about a 3x improvement in performance on my
10-thread machine. The serial implementation gives an average ~0.189s~ total runtime, while the below
parallel implementation runs in about ~0.066s~ after the cpu cache has filled on the first run.
#+BEGIN_SRC c
#include <math.h>
#include <omp.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define matrix_dimension 700
int n = matrix_dimension;
float sum;
int main() {
float A[n][n];
float x0[n];
float b[n];
float x1[n];
float res[n];
srand((unsigned int)(time(NULL)));
// not worth parallellization - rand() is not thread-safe
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = ((float)rand() / (float)(RAND_MAX) * 5.0);
}
x0[i] = ((float)rand() / (float)(RAND_MAX) * 5.0);
}
#pragma omp parallel for private(sum)
for (int i = 0; i < n; i++) {
sum = 0.0;
for (int j = 0; j < n; j++) {
sum += fabs(A[i][j]);
}
A[i][i] += sum;
}
#pragma omp parallel for private(sum)
for (int i = 0; i < n; i++) {
sum = 0.0;
for (int j = 0; j < n; j++) {
sum += A[i][j];
}
b[i] = sum;
}
float tol = 0.0001;
float error = 10.0 * tol;
int maxiter = 100;
int iter = 0;
while (error > tol && iter < maxiter) {
#pragma omp parallel for
for (int i = 0; i < n; i++) {
float temp_sum = b[i];
for (int j = 0; j < n; j++) {
temp_sum -= A[i][j] * x0[j];
}
res[i] = temp_sum;
x1[i] = x0[i] + res[i] / A[i][i];
}
sum = 0.0;
#pragma omp parallel for reduction(+ : sum)
for (int i = 0; i < n; i++) {
float val = x1[i] - x0[i];
sum += val * val;
}
error = sqrt(sum);
#pragma omp parallel for
for (int i = 0; i < n; i++) {
x0[i] = x1[i];
}
iter++;
}
for (int i = 0; i < n; i++)
printf("x[%d] = %6f \t res[%d] = %6f\n", i, x1[i], i, res[i]);
return 0;
}
#+END_SRC
* Question Two
I only see lowerings in performance (likely due to overhead) on my machine using OpenMP until
~matrix_dimension~ becomes quite large, about ~300~ in testing. At ~matrix_dimension=1000~, I see another
about 3x improvement in total runtime (including initialization & I/O which was untouched, so, even further
improvements could be made) on my 10-thread machine; from around ~0.174~ seconds to ~.052~.
#+BEGIN_SRC c
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#ifdef _OPENMP
#include <omp.h>
#else
#define omp_get_num_threads() 0
#define omp_set_num_threads(int) 0
#define omp_get_thread_num() 0
#endif
#define matrix_dimension 1000
int n = matrix_dimension;
float ynrm;
int main() {
float A[n][n];
float v0[n];
float v1[n];
float y[n];
//
// create a matrix
//
// not worth parallellization - rand() is not thread-safe
srand((unsigned int)(time(NULL)));
float a = 5.0;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = ((float)rand() / (float)(RAND_MAX)*a);
}
v0[i] = ((float)rand() / (float)(RAND_MAX)*a);
}
//
// modify the diagonal entries for diagonal dominance
// --------------------------------------------------
//
for (int i = 0; i < n; i++) {
float sum = 0.0;
for (int j = 0; j < n; j++) {
sum = sum + fabs(A[i][j]);
}
A[i][i] = A[i][i] + sum;
}
//
// generate a vector of ones
// -------------------------
//
for (int j = 0; j < n; j++) {
v0[j] = 1.0;
}
//
// power iteration test
// --------------------
//
float tol = 0.0000001;
float error = 10.0 * tol;
float lam1, lam0;
int maxiter = 100;
int iter = 0;
while (error > tol && iter < maxiter) {
#pragma omp parallel for
for (int i = 0; i < n; i++) {
y[i] = 0;
for (int j = 0; j < n; j++) {
y[i] = y[i] + A[i][j] * v0[j];
}
}
ynrm = 0.0;
#pragma omp parallel for reduction(+ : ynrm)
for (int i = 0; i < n; i++) {
ynrm += y[i] * y[i];
}
ynrm = sqrt(ynrm);
#pragma omp parallel for
for (int i = 0; i < n; i++) {
v1[i] = y[i] / ynrm;
}
#pragma omp parallel for
for (int i = 0; i < n; i++) {
y[i] = 0.0;
for (int j = 0; j < n; j++) {
y[i] += A[i][j] * v1[j];
}
}
lam1 = 0.0;
#pragma omp parallel for reduction(+ : lam1)
for (int i = 0; i < n; i++) {
lam1 += v1[i] * y[i];
}
error = fabs(lam1 - lam0);
lam0 = lam1;
#pragma omp parallel for
for (int i = 0; i < n; i++) {
v0[i] = v1[i];
}
iter++;
}
printf("in %d iterations, eigenvalue = %f\n", iter, lam1);
}
#+END_SRC
* Question Three
[[https://static.simponic.xyz/lizfcm.pdf]]

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% Created 2023-12-11 Mon 19:24
% 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 9}
\hypersetup{
pdfauthor={Elizabeth Hunt},
pdftitle={Homework 9},
pdfkeywords={},
pdfsubject={},
pdfcreator={Emacs 28.2 (Org mode 9.7-pre)},
pdflang={English}}
\begin{document}
\maketitle
\setlength\parindent{0pt}
\section{Question One}
\label{sec:org69bed2d}
With a \texttt{matrix\_dimension} set to 700, I consistently see about a 3x improvement in performance on my
10-thread machine. The serial implementation gives an average \texttt{0.189s} total runtime, while the below
parallel implementation runs in about \texttt{0.066s} after the cpu cache has filled on the first run.
\begin{verbatim}
#include <math.h>
#include <omp.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define matrix_dimension 700
int n = matrix_dimension;
float sum;
int main() {
float A[n][n];
float x0[n];
float b[n];
float x1[n];
float res[n];
srand((unsigned int)(time(NULL)));
// not worth parallellization - rand() is not thread-safe
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = ((float)rand() / (float)(RAND_MAX) * 5.0);
}
x0[i] = ((float)rand() / (float)(RAND_MAX) * 5.0);
}
#pragma omp parallel for private(sum)
for (int i = 0; i < n; i++) {
sum = 0.0;
for (int j = 0; j < n; j++) {
sum += fabs(A[i][j]);
}
A[i][i] += sum;
}
#pragma omp parallel for private(sum)
for (int i = 0; i < n; i++) {
sum = 0.0;
for (int j = 0; j < n; j++) {
sum += A[i][j];
}
b[i] = sum;
}
float tol = 0.0001;
float error = 10.0 * tol;
int maxiter = 100;
int iter = 0;
while (error > tol && iter < maxiter) {
#pragma omp parallel for
for (int i = 0; i < n; i++) {
float temp_sum = b[i];
for (int j = 0; j < n; j++) {
temp_sum -= A[i][j] * x0[j];
}
res[i] = temp_sum;
x1[i] = x0[i] + res[i] / A[i][i];
}
sum = 0.0;
#pragma omp parallel for reduction(+ : sum)
for (int i = 0; i < n; i++) {
float val = x1[i] - x0[i];
sum += val * val;
}
error = sqrt(sum);
#pragma omp parallel for
for (int i = 0; i < n; i++) {
x0[i] = x1[i];
}
iter++;
}
for (int i = 0; i < n; i++)
printf("x[%d] = %6f \t res[%d] = %6f\n", i, x1[i], i, res[i]);
return 0;
}
\end{verbatim}
\section{Question Two}
\label{sec:orgbeace25}
I only see lowerings in performance (likely due to overhead) on my machine using OpenMP until
\texttt{matrix\_dimension} becomes quite large, about \texttt{300} in testing. At \texttt{matrix\_dimension=1000}, I see another
about 3x improvement in total runtime (including initialization \& I/O which was untouched, so, even further
improvements could be made) on my 10-thread machine; from around \texttt{0.174} seconds to \texttt{.052}.
\begin{verbatim}
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#ifdef _OPENMP
#include <omp.h>
#else
#define omp_get_num_threads() 0
#define omp_set_num_threads(int) 0
#define omp_get_thread_num() 0
#endif
#define matrix_dimension 1000
int n = matrix_dimension;
float ynrm;
int main() {
float A[n][n];
float v0[n];
float v1[n];
float y[n];
//
// create a matrix
//
// not worth parallellization - rand() is not thread-safe
srand((unsigned int)(time(NULL)));
float a = 5.0;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
A[i][j] = ((float)rand() / (float)(RAND_MAX)*a);
}
v0[i] = ((float)rand() / (float)(RAND_MAX)*a);
}
//
// modify the diagonal entries for diagonal dominance
// --------------------------------------------------
//
for (int i = 0; i < n; i++) {
float sum = 0.0;
for (int j = 0; j < n; j++) {
sum = sum + fabs(A[i][j]);
}
A[i][i] = A[i][i] + sum;
}
//
// generate a vector of ones
// -------------------------
//
for (int j = 0; j < n; j++) {
v0[j] = 1.0;
}
//
// power iteration test
// --------------------
//
float tol = 0.0000001;
float error = 10.0 * tol;
float lam1, lam0;
int maxiter = 100;
int iter = 0;
while (error > tol && iter < maxiter) {
#pragma omp parallel for
for (int i = 0; i < n; i++) {
y[i] = 0;
for (int j = 0; j < n; j++) {
y[i] = y[i] + A[i][j] * v0[j];
}
}
ynrm = 0.0;
#pragma omp parallel for reduction(+ : ynrm)
for (int i = 0; i < n; i++) {
ynrm += y[i] * y[i];
}
ynrm = sqrt(ynrm);
#pragma omp parallel for
for (int i = 0; i < n; i++) {
v1[i] = y[i] / ynrm;
}
#pragma omp parallel for
for (int i = 0; i < n; i++) {
y[i] = 0.0;
for (int j = 0; j < n; j++) {
y[i] += A[i][j] * v1[j];
}
}
lam1 = 0.0;
#pragma omp parallel for reduction(+ : lam1)
for (int i = 0; i < n; i++) {
lam1 += v1[i] * y[i];
}
error = fabs(lam1 - lam0);
lam0 = lam1;
#pragma omp parallel for
for (int i = 0; i < n; i++) {
v0[i] = v1[i];
}
iter++;
}
printf("in %d iterations, eigenvalue = %f\n", iter, lam1);
}
\end{verbatim}
\section{Question Three}
\label{sec:org33439e0}
\url{https://static.simponic.xyz/lizfcm.pdf}
\end{document}