Media Summary: ... and Almir Mutapcic Name of the paper: Neither the lasso nor the SVM objective function is differentiable, and we had to do some work for each to optimize with ... Hope you will enjoy this video. I know my voiceover is lacking some emotion but i will try my best to improve that for my next video.
Linma2491 Subgradient Methods - Detailed Analysis & Overview
... and Almir Mutapcic Name of the paper: Neither the lasso nor the SVM objective function is differentiable, and we had to do some work for each to optimize with ... Hope you will enjoy this video. I know my voiceover is lacking some emotion but i will try my best to improve that for my next video. Um I'm going to talk about subradiant and F of X bar it is finite it's less than infinity okay an element V in R is current a Note: sound cuts out for last 20 minutes or so, sorry!
I recommend you watch in 1.25x or 1.5x to not waste time. Um okay another thing we get from this balance is actually the convergence rate of In this talk spanning about 36 minutes we discuss an issue which lies at the heart of modern convex optimization algorithms. Okay um so basically that said here all i'm doing is applying the In this video we start looking at non-smooth optimization. We take a look at the subdifferential set and several examples of ... ... same logic for the uh Vector of the optimality cut now um here the difference with the LJ
Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department.
