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.
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Short and Excellent. I finally get this now. Very straightforward. Thank You.
Really well explained, thank you
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Very helpful content! Easy to understand, right to the point! Thank you so much for posting this, +1 sub!
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thank you for the series !
incredible video, thanks
Another home run. I'm finding Data Daft is my go-to if there's a choice between content creators
Thanks for the video!
Very well-explained! Kudos!
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)
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Very useful and well explained. Thank you.
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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.
great explanation