What is the big deal about Big-O notation in computer science?

Question

How would Big-O notation help in my day-to-day C# programming? Is it just an academic exercise?

Answer 1

This is a question that (Almost) everyone asks during their CS studies, especially if they plan to be industrial developers.

As everyone here indicated, yes, it's critical. Although you might be able to evade it, or never care about performance, at some point you're going to be affected by it. At some point you will have to manipulate a lot of data in memory, and you will have to find a way to do it efficiently. You will have to choose between existing collections in some cases, and in others will have to design your own.

That being said, I have found that some schools push too much the mathematical/algebraic side to their undergraduates over the importance for real world use. Students who are less interested in this algebraic side develop a distaste. IMHO, there is no need for most CS students to know how to calculate Big O beyond the basics. Forcing things like the Masters theorem down their throat is not going to make them appreciate this.

Answer 2

Remember that big-O tells you how algorithms scale with large numbers of inputs, it doesn't tell you witch algorithm is faster for your task.

Building pyramids is O(n) while sorting pictures of them is, at best, O(n log n) it doesn't mean it's quicker to build them than make a slide shoow.

Answer 3

Big-O tells you the complexity of an algorithm in terms of the size of its inputs. This is essential if you want to know how algorithms will scale. If you're designing a big website and you have a lot of users, the time it takes you to handle those requests is important. If you have lots of data and you want to store it in a structure, you need to know how to do that efficiently if you're going to write something that doesn't take a million years to run.

It's not that Big-O notation itself will help you. It's that if you understand Big-O notation, you understand the worst-case complexity of algorithms. Essentially, Big-O gives you a high-level sense of which algorithms are fast, which are slow, and what the tradeoffs are. I don't see how you can understand the performance implications of anything in, say, the .NET collections library if you don't understand this.

I won't go into more detail here, since this question has been asked many times, but suffice it to say that this is something you should understand. Here's a fairly highly voted previous Big-O question to get you started.

Answer 4

I am reading answers and I (seriously) think that big-O is underestimated.

As coders who make money from coding, we need to know what big-O is and why we need it.

Let me explain what I think: Big-O notation is the efficiency/performance of your work. You have to know how fast your code works when the inputs get bigger because in real life you can't know the exact number of inputs. Furthermore, you can't compare two different algorithmic approaches without an asymptotic notation so if you want to choose the better one, you are going to compare them with big-O and see which one fits your situation. Both may be inefficient but you will know which one is better.

Answer 5

Knowing what the relative strengths and weaknesses of different types of containers and sort algorithms helps you choose the correct one for the current situation. Big O notation is a convenient way to express the major difference, the algorithmic time complexity.

Answer 6

No, it really helps to know what the efficiency of different algorithms are.

If you take the time to understand Big O, every time you sit down to code a loop, you'll be thinking "How can I make this more efficient?" - which is a good thing :)