Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization
Abstract
Making the gradients small is a fundamental optimization problem that has eluded unifying and simple convergence arguments in first-order optimization, so far primarily reserved for other convergence criteria, such as reducing the optimality gap. We introduce a novel potential function-based framework to study the convergence of standard methods for making the gradients small in smooth convex optimization and convex-concave min-max optimization. Our framework is intuitive and it provides a lens for viewing algorithms that make the gradients small as being driven by a trade-off between reducing either the gradient norm or a certain notion of an optimality gap. On the lower bounds side, we discuss tightness of the obtained convergence results for the convex setup and provide a new lower bound for minimizing norm of cocoercive operators that allows us to argue about optimality of methods in the min-max setup.
Cite
@article{arxiv.2101.12101,
title = {Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization},
author = {Jelena Diakonikolas and Puqian Wang},
journal= {arXiv preprint arXiv:2101.12101},
year = {2021}
}