Date: Fri, 13 Oct 2017 15:00:00 +0000
<p class="p1"><span class="s1">How long an algorithm takes to run depends on many factors including implementation details and hardware.<span class="Apple-converted-space"> </span> However, the formal analysis of algorithms focuses on how they will perform in the worst case as the input size grows.<span class="Apple-converted-space"> </span> We refer to an algorithm's runtime as it's "O" which is a function of its input size "n".<span class="Apple-converted-space"> </span> For example, O(n) represents a linear algorithm - one that takes roughly twice as long to run if you double the input size.<span class="Apple-converted-space"> </span> In this episode, we discuss a few everyday examples of algorithmic analysis including sorting, search a shuffled deck of cards, and verifying if a grocery list was successfully completed.</span></p> <p class="p1"><span class="s1">Thanks to our sponsor <a href="https://brilliant.org/dataskeptics">Brilliant.org</a>, who right now is featuring a related problem as their <a href="https://brilliant.org/problems/how-likely-is-a-flush/">Brilliant Problem of the Week</a>.</span></p>
