About Me

My photo
I'm a colonist who has declared war on machines and intend to conquer them some day. You'll often find me deep in the trenches fighting off bugs and ugly defects in code. When I'm not tappity-tapping at my WMD (also, known as keyboard), you'll find me chatting with friends, reading comics or playing a PC game.

Thursday, June 5, 2008

Sorting Stuff with Heap Sort - Part I

I’m starting a small series of blog posts that will explain the Heap Sort algorithm in depth. I’ve noticed that this is an academic topic that few are willing to touch but I am! I hope you find this short series informative.

The Basics.
First, lets get through the basics of any sorting algorithm. A sorting algorithm is an algorithm that seeks to arrange the elements of a data structure (think, a simple List) in a particular order. In most cases, we prefer the elements to be sorted in either ascending or descending order. The efficiency of an algorithm can be measured in terms of its Time Complexity or Space Complexity. The term Time Complexity refers to the amount of time that an algorithm takes to process the input and produce the desired output. The term Space Complexity refers to the amount of space or memory that an algorithm takes to produce the expected result. Generally, the complexity of an algorithm (time or space) is expressed as a function of the size of the problem, n. This is known as the Big-Oh Notation. So, if we were to say that a sorting algorithm has a time complexity of O(n), it means that the algorithm will take a factor of 10 units of time to sort 10 values. If the complexity of the algorithm were O(n2), then it would take a factor of 100 units of time to sort 10 values. Note that I have said that the algorithm will take "a factor of" time because this factor would depend on the particular problem. For example, if we were sorting simple integers, we could expect this factor to be a lot less than if we were sorting strings. Also, the complexity of an algorithm can be specified for Best, Worst or Average case scenarios. A best case scenario for a sorting algorithm would, of course, be if the data were already sorted in the first place. A worst case for a sorting algorithm would arise if the data were arranged in a manner that is contrary to the working of the algorithm. Generally, a good algorithm is measured for its worst-case scenario but in some cases, we also consider the average case.

Heap Sort is an O(nLogn) time complexity algorithm. This is in stark contrast with other sorting algorithms such as Bubble Sort (the unjustifiably most famous sort) which has an average or worst case complexity of O(n2). In my next post, we will dive into the Heap Sort algorithm and find out the reason as to why its called Heap Sort in the first place.

No comments: