%0 Journal Article
%T A Comparison of First-order Algorithms for Machine Learning
%A Yu Wei
%A Pock Thomas
%J Computer Science
%D 2014
%I arXiv
%X Using an optimization algorithm to solve a machine learning problem is one of mainstreams in the field of science. In this work, we demonstrate a comprehensive comparison of some state-of-the-art first-order optimization algorithms for convex optimization problems in machine learning. We concentrate on several smooth and non-smooth machine learning problems with a loss function plus a regularizer. The overall experimental results show the superiority of primal-dual algorithms in solving a machine learning problem from the perspectives of the ease to construct, running time and accuracy.
%U http://arxiv.org/abs/1404.6674v1