Line Search and Trust-Region Methods for Convex-Composite Optimization
Optimization and Control
2019-09-11 v2
Abstract
We consider descent methods for solving non-finite valued nonsmooth convex-composite optimization problems that employ Gauss-Newton subproblems to determine the iteration update. Specifically, we establish the global convergence properties for descent methods that use a backtracking line search, a weak Wolfe line search, or a trust-region update. All of these approaches are designed to exploit the structure associated with convex-composite problems.
Cite
@article{arxiv.1806.05218,
title = {Line Search and Trust-Region Methods for Convex-Composite Optimization},
author = {James V. Burke and Abraham Engle},
journal= {arXiv preprint arXiv:1806.05218},
year = {2019}
}