On Recent Trends in Extremely Large-Scale Convex Optimization
published: Jan. 19, 2010, recorded: December 2009, views: 922
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.
In the talk, we focus on algorithms for solving well-structured large-scale convex programs in the case where huge problem's sizes prevent processing it by polynomial time algorithms and thus make computationally cheap first order optimization methods the methods of choice. We overview significant recent progress in utilizing problem's structure within the first order framework, with emphasis on algorithms with dimension-independent (and optimal in the large-scale case) iteration complexity being the target accuracy. We then discuss the possibility to further accelerate the first order algorithms by randomization, specifically, by passing from expensive in the extremely large scale case precise deterministic first order oracles to their computationally cheap stochastic counterparts. Applications to be discussed include SVM's, minimization, testing sensing matrices for "goodness" in the Compressed Sensing context, low-dimensional approximation of high-dimensional samples, and some others.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !