December 16, 2015 13:41
Dr. Lee presented his recent research about proximal point algorithms to solve nonsmooth convex penalized regression problems, in the 8th International Conference on the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2015), London UK, Dec 12-14 (http://www.cmstatistics.org/CMStatistics2015/), in the session EO150: Convex optimization in statistics. Dr. Lee was invited by the session organizer Prof. Keith Knight from Department of Statistics, University of Toronto.
Accelerated proximal point methods for solving penalized regression problems
Efficient optimization methods to obtain solutions of penalized regression problems, especially in high dimensions, have been studied quite extensively in recent years, with their successful applications in machine learning, image processing, compressed sensing, and bioinformatics, just to name a few. Amongst them, proximal point methods and their accelerated variants have been quite competitive in many cases. These algorithms make use of special structures of problems, e.g. smoothness and separability, endowed by the choices of loss functions and regularizers. We will discuss two types of first-order proximal point algorithms, namely accelerated proximal gradient descent and accelerated proximal extra gradient techniques, focusing on the latter, in the context of Lasso and generalized Dantzig selector.
