A proximal method for composite minimization
Optimization and Control
2015-04-24 v2 Numerical Analysis
Abstract
We consider minimization of functions that are compositions of convex or prox-regular functions (possibly extended-valued) with smooth vector functions. A wide variety of important optimization problems fall into this framework. We describe an algorithmic framework based on a subproblem constructed from a linearized approximation to the objective and a regularization term. Properties of local solutions of this subproblem underlie both a global convergence result and an identification property of the active manifold containing the solution of the original problem. Preliminary computational results on both convex and nonconvex examples are promising.
Cite
@article{arxiv.0812.0423,
title = {A proximal method for composite minimization},
author = {A. S. Lewis and S. J. Wright},
journal= {arXiv preprint arXiv:0812.0423},
year = {2015}
}