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i am gonna throw up, how was i supposed to come up with the last bit of logic in the last problem

Excellent work mate. :)

sign for imaginary components are not matching with numpy.fft.fft() results!

Amazing!

kewl

I love when algorithm's names have simple words in them such as this one's `fast` or SHA-256's acronym's `secure hash algorithm`. Truly the most ingenious aspect of these algorithms is their names.

It is so easy, you move the smallest disk every second move in a sequence from 1-2 and 2-3 and 3-1 etc. Very boring. In the other moves there are only one choice to move when you must put the smaller disk on to the larger. That's it. 30 seconds to find out.

When in high-school I used to spend the time in transit by doing the tower in the head. My record was 8 disks before arriving but it heavily depended on the number of pretty girls that sometimes made it difficult to concentrate.

What an incredible video, thank you for the content, really well explained! 😁

greatly explanied!

what does the line y= [0]*n indicate in fft function ?? It is not very clear to me.

excellent tutorial

great video, thank you!!

great video! but can you please teach us how to identify if a given problem can be solved by recursion or not?

90% of the people still doesn't know how to solve this type of the problems.

is the maze solvable?

thanks for clear explaining

fantastic video! respect++ just an optimization for the last problem's base case currently, say if I provide n=3 and m=15, it will return this : { 0 (since n<m) + f(n, m-1)} == {0 + f(3, 14)} and f(3,14) will again return {0 + f(3, 13)} and these unnecessary calls will continue untill m==n i.e. untill f(3,3) gets called, so we can add a line: if(n<m) return f(n,m) to reduce time waste and jump to effective function calls

Best explanation of TOH I've ever heard.

huh

Reducible, cool video keep up the amazing work

this video is oddly beautiful, i absolutely despised this whole semester of discrete math but you kinda got me invested

what about webp

am i stupid or it is hard?

best explanantion of this concept and also the most beauiful in any book or video ever!!!!

Who else came here after watching Nvidia's CEO talk about the cuOPT (Combinatorial Optimization) library

This was a great video...I'm trying to "figure this stuff out" and so when i learned there is a word to describe one of the other issues i had noticed because it was so common (aka tunneling).

I learnt a lot with this video. Ive tried my solution of those problem in C++ but there is something i dont know how to handle. With Voloban equation, only the speed vector is changed then the position is changed by another function. It wont make a long time until, when particle A and B collides the algo moves one of them in particle C.... After that we have the opposite effect : Part a (or b) is now glued to part c. I would love to find a way to implement this correctly...

I got lost at about 15 minutes. Something is really missing in this explanation

What does the DAG look like for the House Robber problem?

I love QOI so much, its such a cool format. And on top of that such a great example of the idiom: "a wise person simplifies the complex whilst a foolish person complicates the simple" (Not to throw shade on the PNG guys, as it's still absolutely mind-boggling how highly it can compress images)

Could you do some mathemagics if you knew the 'negative' volume that makes a shape concave when doing the Minkowski sums?

Very nicely introduced, I hope you have some very complex cutting edge applications of graph theory to show off :)

This video helped my understand my bugs alot better thanks!!

u can also solve it using loops

in the last problem shouldnt we also include that count_partitions(n,m) -> 1 if n=0 AND m!=0? (since m is already assumed to be >=0)

It was nt that my mind was nt blown. It was. But I started weeping too! Really cool!

For finding if point A passed the origin, would it not be simpler to just check the signs of the relevant vector components?

I love the way how he discretely changed it to P[::2] and P[1::2] to match python syntax.

20:49 unopened brackets when saying to me to check if it is true? Are you kidding me?

Can someone please explain how did the multiplication yield those results at 6:31

Very cool 👌… i will try this volume bounding thingy sound not impossible to code. Thanks!! Also the sweep was really well explained i was using it without fully understand! ❤

I did this iteratively, fear me.

God bless you bro ❤

Such a shame this video hasn't millions of views. I'm not kidding, we're looking at a masterpiece.

Holy fuck, I am in awe ! Damn, our prof covered this in a week and I couldn't understand how it really worked so I have to revisit such lectures regularly Thanks

Imagine bloating matrix representation to 30 minutes and still confusing "people." Covid didn't go far enough curing cancer.

"Just because a question is easy to verify doesn't mean it's easy to solve." prove it and you'll win $1M; that's literally the P v NP problem.

You are so geneous

@10:32 the succinctness ends here. how is the split up representation having squares in the x's?