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.

[@ivannuzhyn4668]

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.

[@salimdellali1814]

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

[@Vartierer]

Really well explained, thank you

[@marclennardcolina6033]

Very well-explained! Kudos!

[@muratseker6406]

thank you for the series !

[@felipeazevedo2279]

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

[@shashikantdivekar7839]

Very useful and well explained. Thank you.

[@codeZarathustra]

Great video, thank very much!

[@peters5171]

Very helpful! Thank you very much

[@targetdexter]

Awesome video! Thank you

[@Swangorapofficial]

Thanks for the video!

[@juliosguido1992]

incredible video, thanks

[@jacklee6106]

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

[@yesyas5972]

Nice video

[@shahbajsingh4228]

Great explanations

[@tomaspiskule3048]

useful explanation, thanks

[@eva42sh]

great explanation

[@jas.moreira]

Thank you!

[@mareksmidrkal9754]

Best explanation ever. Thanks ! @.@

[@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)

[@MatthewGDunlap]

Thanks.

[@proterotype]

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

[@SkielCast]

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

[@DataDaft]

I believe brute force solutions to it are O(n!) but better solutions that use methods like dynamic programming are exponential times a polynomial factor ~O(2^n * n²) which is usually just described as being in the class of exponential. You can find various articles on this: medium.com/basecs/speeding-up-the-traveling-salesman-using-dynamic-programming-b76d7552e8dd

[@SkielCast]

@@DataDaft Thank you so much for your answer and for the article! The video was awesome

[@eva42sh]

great explanation