Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning
published: Jan. 19, 2010, recorded: December 2009, views: 613
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We analyze the convergence behaviour of a recently proposed algorithm for sparse learning called Dual Augmented Lagrangian (DAL). We theoretically analyze under some conditions that DAL converges super-linearly in a non-asymptotic and global sense. We experimentally confirm our analysis in a large scale ℓ1-regularized logistic regression problem and compare the efficiency of DAL algorithm to existing algorithms.
Download slides: nipsworkshops09_tomioka_slc_01.pdf (617.0 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !