😂

That just depends on your math, for example you could multiply any z axis distance by some arbitrary value to weight inclines more heavily. If the noise distortion is sufficient then one of the random paths would probably eventually skirt around to either side and that would become the new shortest path.

​@@OGPatriot03 but if the noise is too strong and big you'll get poor accuracy toward the end. Ideally the noise scaling and strength should decrease overtime in order to get good exploration at the start and small distortion in the end.

one approach to help with that which wouldn't be too hard to fit into this would be to always accept changes which reduce the length, but also possibly accept changes which increase it, but with a chance which goes down the larger the increase, instead of simply adding more noise. This means the path still strongly favors becoming shorter, but still retains some chance to wander about. You could also keep track of what the shortest path found was, besides the current path.

Exactly, why not just blur position while «pinning» endpoints with spline parameter?

@@namor_votilav that won't work for most metrics, will it? Like, if you're using euclidian, which is the usual, it should work, but for anything else I think it would fail

thanks for saving me using map range everywhere

Could you tell in detail how would implementation be exactly?

It's a technique used in other areas of computer science, and there are many variations to try to find better extremas

But in every task you somehow find a smart solution!

I feel like an idiot watching all of his videos

what do you mean by x1, y10, etc?

@@user-sl6gn1ss8p As in draw a line between the points 1,0 to 0,10 then again between 2,0 and 0,9 and again from 3,0 to 0,8 etc.. If you then imagined drawing an infinite amount of these lines (in this case the step =1) but if we used a step that was infinitely small we would get a smooth curve. And also im pretty sure the name of this curve will be a quadratic Bézier curve since what you are doing is essentially evaluating nested lerp functions. It is pretty much a geometric interpretation of De Casteljau's algorithm on paper - very cool imo and hope this helps :)

@@rubenoconnor198 I think I'm misundertanding something, the way I got it this would make a curve mirrored in the y axis. Which is still kinda cool tho

People have already done this in shader nodes

@@samk9632 lol i'm not surprised

math is math it is calc of var at its core sure, its basically a minimization function but it gives results consistent with calc of var

Yeah thought same thing, do you know any better algorithm to that else? Any tutorial source?

Yes. Convex optimization + gradient descent.