A Gentle Introduction to Algorithm Complexity Analysis by Dionysis “dionyziz” Zindros.
From the post:
A lot of programmers that make some of the coolest and most useful software today, such as many of the stuff we see on the Internet or use daily, don’t have a theoretical computer science background. They’re still pretty awesome and creative programmers and we thank them for what they build.
However, theoretical computer science has its uses and applications and can turn out to be quite practical. In this article, targeted at programmers who know their art but who don’t have any theoretical computer science background, I will present one of the most pragmatic tools of computer science: Big O notation and algorithm complexity analysis. As someone who has worked both in a computer science academic setting and in building production-level software in the industry, this is the tool I have found to be one of the truly useful ones in practice, so I hope after reading this article you can apply it in your own code to make it better. After reading this post, you should be able to understand all the common terms computer scientists use such as “big O”, “asymptotic behavior” and “worst-case analysis”.
Do you nod when encountering “big O,” “asymptotic behavior” and “worst-case analysis” in CS articles?
Or do you understand what is meant when you encounter “big O,” “asymptotic behavior” and “worst-case analysis” in CS articles?
You are the only one who can say for sure. If it has been a while or you aren’t sure, this should act as a great refresher.
As an incentive, you can intimidate co-workers with descriptions of your code. 😉
I first saw this in a tweet by Markus Sagebiel.