Algorithms Explained: Computational Complexity

An overview of computational complexity including the basics of big O notation and common time complexities with examples of each.

Understanding computational complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if you don't implement algorithms yourself, an understanding of computational complexity can help you better apply the tools you use.

[ivannuzhyn]

The best explanation I found on youtube. Thanks a lot, finally understand it:)

[RyanAndersonTechnical]

Great summary and refresh - Thanks for posting. 🙏

[femloh]

Short and Excellent. I finally get this now. Very straightforward. Thank You.

[salimdellali]

very helpful, you helped me refresh my knowledge about comlexity, clearly explained, to the point, short and concise. You have my like

[rampravesh]

the best explanation of computational complexity. Thank you very much.

[felipeazevedo]

Very helpful content! Easy to understand, right to the point! Thank you so much for posting this, +1 sub!

[shashikantdivekar]

Very useful and well explained. Thank you.

[jacklee]

Great stuff. Please do more leetcode contents. Keep it up! thanks :D

[proterotype]

Another home run. I'm finding Data Daft is my go-to if there's a choice between content creators

[Kokurorokuko]

You didn't mention O(sqrt(2)) which is rare but also important. It grows faster than O(log(n)) but slower than O(n)

[SkielCast]

Isn't traveling salesman a O(n!) problem? I think the backpack problem was O(2^n)