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
the best explanation of computational complexity. Thank you very much.
The best explanation I found on youtube. Thanks a lot, finally understand it:)
Great summary and refresh - Thanks for posting. 🙏
Short and Excellent. I finally get this now. Very straightforward. Thank You.
very helpful, you helped me refresh my knowledge about comlexity, clearly explained, to the point, short and concise. You have my like
Really well explained, thank you
Very helpful content! Easy to understand, right to the point! Thank you so much for posting this, +1 sub!
Nice video
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)
thank you for the series !
Another home run. I'm finding Data Daft is my go-to if there's a choice between content creators
Very well-explained! Kudos!
Very helpful! Thank you very much
Very useful and well explained. Thank you.
Awesome video! Thank you
Great stuff. Please do more leetcode contents. Keep it up! thanks :D
incredible video, thanks
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Thanks for the video!
great explanation
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great explanation
Thank you!
Isn't traveling salesman a O(n!) problem? I think the backpack problem was O(2^n)
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
@@DataDaft Thank you so much for your answer and for the article! The video was awesome
Thanks.