004 Subgradient Methods For Huge

We consider a new class of

We show two useful properties of the function values for the

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.

Ryan Tibshirani @ Stats, CMU. http://www.stat.cmu.edu/~ryantibs/convexopt/

Neither the lasso nor the SVM objective function is differentiable, and we had to do some work for each to optimize with ...

Note: sound cuts out for last 20 minutes or so, sorry!

Dc

Chapter 5: Convex Numerical algorithms 5.1: The

Ryan Tibshirani @ Stats, CMU. http://www.stat.cmu.edu/~ryantibs/convexopt/

In this video we start looking at non-smooth optimization. We take a look at the

Samantha Clapp, Charles Cratty, Breanna Page Abstract: The phenomenon of Zigzagging of Kind I is present in pure

I recommend you watch in 1.25x or 1.5x to not waste time.

Um I'm going to talk about subradiant and

...

These lectures will cover both basics as well as cutting-edge topics in

Uh no so for the subgrading

Definition of

Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department.

Dive into the

These lectures will cover both basics as well as cutting-edge topics in