A note on the convergence of deterministic gradient sampling in nonsmooth optimization-Reference-Cited by-同舟云学术

A note on the convergence of deterministic gradient sampling in nonsmooth optimization

Published:2024-02-06 Issue:1 Volume:88 Page:151-165
ISSN:0926-6003
Container-title:Computational Optimization and Applications
language:en
Short-container-title:Comput Optim Appl

Author:

Gebken Bennet^ORCID

Abstract

AbstractApproximation 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.

Funder

Universität Paderborn

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10589-024-00552-0.pdf

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