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bobzhangyc

How is Newton's method equation mapped to a disc?

tacocat

How does applying the inverse of the hessian add a quadratic term of optimization?

niyiqiul

Are methods involving higher-order momentums better suited for non-convex problems?

Midoriya

For nonconvex problems, can we do gradient descent with random restarts?

twizzler

Are algorithms from machine learning applied here (random starts, random walking with k walkers, etc)?

aa4

In the lecture, you mentioned that the sign of Hessian flips as we move from x_k to to x*. I didn't quite get why is that the case? Shouldn't it be always positive as it positive semi-definite?

coolpotato

In the video, you said we use the inverse of the Hessian as it would be part of the 2nd order Taylor approximation of f. Would using a higher order derivative than the Hessian further help create that "bowl-like" structure? And if so, what kind of tradeoffs are there? Specifically, does it help save time?

WhaleVomit

Is it practical to use 3rd degree or above order descents?

corgo

How costly would it be to use 3 or above order descents? Is it worth it?