አባል:ADmerphi

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".

This text is also targeted at the junior high school and high school students from Greece or anywhere else internationally competing in the International Olympiad in Informatics, an algorithms competition for students, or other similar competitions. As such, it does not have any mathematical prerequisites and will give you the background you need in order to continue studying algorithms with a firmer understanding of the theory behind them. As someone who used to compete in these student competitions, I highly advise you to read through this whole introductory material and try to fully understand it, because it will be necessary as you study algorithms and learn more advanced techniques.

I believe this text will be helpful for industry programmers who don't have too much experience with theoretical computer science (it is a fact that some of the most inspiring software engineers never went to college). But because it's also for students, it may at times sound a little bit like a textbook. In addition, some of the topics in this text may seem too obvious to you; for example, you may have seen them during your high school years. If you feel you understand them, you can skip them. Other sections go into a bit more depth and become slightly theoretical, as the students competing in this competition need to know more about theoretical algorithms than the average practitioner. But these things are still good to know and not tremendously hard to follow, so it's likely well worth your time. As the original text was targeted at high school students, no mathematical background is required, so anyone with some programming experience (i.e. if you know what recursion is) will be able to follow through without any problem.

Throughout this article, you will find various pointers that link you to interesting material often outside the scope of the topic under discussion. If you're an industry programmer, it's likely that you're familiar with most of these concepts. If you're a junior student participating in competitions, following those links will give you clues about other areas of computer science or software engineering that you may not have yet explored which you can look at to broaden your interests.

Big O notation and algorithm complexity analysis is something a lot of industry programmers and junior students alike find hard to understand, fear, or avoid altogether as useless. But it's not as hard or as theoretical as it may seem at first. Algorithm complexity is just a way to formally measure how fast a program or algorithm runs, so it really is quite pragmatic. Let's start by motivating the topic a little bit.