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authorNavid Samanghoon <nsama24@student.sdu.dk>2025-10-29 13:06:10 +0100
committermithe24 <mithe24@student.sdu.dk>2025-10-29 13:49:57 +0100
commit96be1689719e94c2e0e45d9678cf744e4c02c167 (patch)
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parentb1dce8fb94ee951efb6f15d6fd6142b74f39e7e0 (diff)
downloadsorter-96be1689719e94c2e0e45d9678cf744e4c02c167.tar.gz
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typos and small improvements
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@@ -112,12 +112,10 @@ follow this strict format.
\subsection{Selecting algorithms}
The ability to choose which algorithm to use can be quite beneficial, if you
-know certain aspects about your data. For example \textit{Quicksort} is very
-good with random data, but bad when the data is sorted, and
-\textit{insertionsort} is good with sorted data, but bad with random data. For
-that reason, and because it makes testing different algorithms easier, because
-it is more flexible, the program will be able to run with different algorithms
-passed as arguments.
+know certain aspects about your data. Therefore a design choice has been allowing
+the user to select an algorithm as a command line argument, rather than hard coding
+it. This design decision is great for flexibility, and also supports future exceptions
+of the program.
\subsection{Why Insertionsort and Quicksort?}
Insertion sort and quick sort have been handpicked to research the trade-offs
@@ -125,7 +123,7 @@ between algorithmic simplicity and computational efficiency. Furthermore,
these algorithms are widely different in nature. Insertion sort is an iterative
algorithm, depending primarily on memory usage, registers, and program flow,
while quick sort is a recursive algorithm using the divide and conquer approach.
-This is interesting, because a Quicksort is expected to excel on large random
+This is interesting, because Quicksort is expected to excel on large random
datasets, while insertion sort is more suitable for small or nearly sorted
cases, which is another great motivation to understand what is really
happening when these algorithms are executed, and how the dataset affects them.