简介: |
Abstract:
This talk overviews a class of algorithms called coordinate descent algorithms and also discusses its recent progress. This class of algorithms has recently gained popularity due to their effectiveness in solving large-scale optimization problems in machine learning, compressed sensing, and image processing. Coordinate descent algorithms solve optimization problems by successively minimizing along each coordinate, or block of coordinates, which is ideal for parallelized and distributed computing. This talk gives relevant theory and examples about how to effectively apply coordinate descent to modern problems in data science and engineering, how to linearly speed up the algorithm by asynchronous parallel computing, and how to obtain global optimality guarantees from those on each coordinate. |
