From e1ea587480a863f073f7cdfdede09819d8282708 Mon Sep 17 00:00:00 2001
From: Andreas Kapp Lindquist <andkaplin05@gmail.com>
Date: Wed, 29 Oct 2025 10:52:29 +0100
Subject: report(*): idk stuff

---
 report/report.tex | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/report/report.tex b/report/report.tex
index 2f414b6..b14fcab 100644
--- a/report/report.tex
+++ b/report/report.tex
@@ -191,6 +191,9 @@ the array format. All functions in this program follow the x86 calling
 conventions, with a few exceptions where all registers are preserved. Below is a
 detailed explanation of some of the more interesting functions.
 
+\subsection{Array maker}
+\snippet{19}{46}{../src/array_maker.s}
+
 \subsection{Insertion-sort}
 This is a classic implementation of the iterative insertion-sort. The function
 takes three arguments: address of the array of pointers to each coordinate,
@@ -263,7 +266,8 @@ the output from the \textit{builtin} function \textit{sort}.
 Given all of the tests pass, we can then run \textit{benchmark.sh}, which times
 how long it takes for our program to run with each test file for each algorithm,
 and saves the result in a \textit{csv} file. The results of this \textit{csv}
-file is shown 
+file, together with functions asymptotically equivalent to $\mathcal O(n)$, $\mathcal O(n\log
+n)$ and $\mathcal O(n^2)$, is shown on \autoref{fig:benchmark}.
 
 \begin{figure}[H]
     \centering
@@ -294,17 +298,17 @@ file is shown
             qsort\_reverse, qsort\_sorted}
 
         % -- O(n)
-        \addplot[domain=1:92709, samples=100, thick, densely dotted, black]
+        \addplot[domain=1:100000, samples=100, ultra thick, densely dotted, black]
           {(x * 1/18000000+0.0003)};
         \addlegendentry{$\mathcal O(n)$}
 
         % -- O(nlogn)
-        \addplot[domain=1:92709, samples=100, thick, dotted, black]
+        \addplot[domain=1:100000, samples=100, ultra thick, dotted, black]
           {(x*ln(x)) * 1/80000000 + 0.0003};
         \addlegendentry{$\mathcal O(n\log n)$}
 
         % -- O(n^2)
-        \addplot[domain=1:92709, samples=100, thick, dashed, black]
+        \addplot[domain=1:100000, samples=100, ultra thick, dashed, black]
           {(x*x) * 1/5000000000+0.0003};
         \addlegendentry{$\mathcal O(n^2)$}
 
@@ -314,6 +318,22 @@ file is shown
     sorted and reverse sorted data.}\label{fig:benchmark}
 \end{figure}
 
+From \autoref{fig:benchmark} we see that Insertionsort behaves as expected
+with a best-case runtime of $\mathcal O(n)$ when the input is sorted, and a
+worst-case runtime of $\mathcal O(n^2)$, when the input reversely sorted or
+random. We also see that Quicksort behaves as expected with a best-case runtime
+of $\mathcal O(n\log n)$ when the input is random, and a worst-case runtime of
+$\mathcal O(n^2)$ when the input is either sorted or reversely sorted.
+
+Footnote
+
+isort\_random,1000000,386.935
+qsort\_random,1000000,0.148
+isort\_random,1000000,343.616
+qsort\_random,1000000,0.159
+isort\_random,1000000,384.022
+qsort\_random,1000000,0.155,0.146
+
 
 \section{Conclusion}
     
-- 
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