The proximal point method revisited
Optimization and Control
2017-12-19 v1
Abstract
In this short survey, I revisit the role of the proximal point method in large scale optimization. I focus on three recent examples: a proximally guided subgradient method for weakly convex stochastic approximation, the prox-linear algorithm for minimizing compositions of convex functions and smooth maps, and Catalyst generic acceleration for regularized Empirical Risk Minimization.
Cite
@article{arxiv.1712.06038,
title = {The proximal point method revisited},
author = {Dmitriy Drusvyatskiy},
journal= {arXiv preprint arXiv:1712.06038},
year = {2017}
}
Comments
11 pages, submitted to SIAG/OPT Views and News