A note on the convergence of deterministic gradient sampling in nonsmooth optimization
Abstract
Approximation of subdifferentials is one of the main tasks when computing descent directions for nonsmooth optimization problems. In this article, we propose a bisection method for weakly lower semismooth functions which is able to compute new subgradients that improve a given approximation in case a direction with insufficient descent was computed. Combined with a recently proposed deterministic gradient sampling approach, this yields a deterministic and provably convergent way to approximate subdifferentials for computing descent directions.
Cite
@article{arxiv.2312.12032,
title = {A note on the convergence of deterministic gradient sampling in nonsmooth optimization},
author = {Bennet Gebken},
journal= {arXiv preprint arXiv:2312.12032},
year = {2024}
}
Comments
This version of the article has been accepted for publication after peer review, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10589-024-00552-0