Subgradient Method Vi Conclusion

We summarise the analysis of 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.

Okay so and we just state the result and we don't have to do anything about the the

Chapter 5: Convex Numerical algorithms 5.1: The

We show two useful properties of the

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

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

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

Lecture 7 Subgradient Method

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

Neither the lasso nor the SVM objective

We formulate the

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We derive a fundamental inequality and derive from it the statement that the distance to any solution is convergent and in ...

... the definition of sub gradients V here is a

...

Uh no so for the subgrading

... going to prove the second

Chapter 5: Convex Numerical algorithms 5.1: The

In this video we start looking at non-smooth