Jan Mankau | Searcharxiv

arXiv2019

A nonsmooth nonconvex descent algorithm

The paper presents a new descent algorithm for locally Lipschitz continuous functions $f:X\to\mathbb{R}$. The selection of a descent direction at some iteration point $x$ combines an approximation of the set-valued gradient of $f$ on a suitable neighborhood of $x$ (recently introduced by Mankau & Schuricht) with an Armijo type step control. The algorithm is analytically justified and it is shown that accumulation points of iteration points are critical points of $f$. Finally the algorithm is tested for numerous benchmark problems and the results are compared with simulations found in the literature.

math.NA↗

arXiv2018

Gradients on Sets

For a locally Lipschitz continuous function $f:X\to\mathbb{R}$ the generalized gradient $\partial f(x)$ of Clarke is used to develop some (set-valued) gradient on a set $A\subset X$. Existence, uniqueness and some approximation are considered for optimal descent directions on set $A$. The results serve as basis for nonsmooth numerical descent algorithms that can be found in subsequent papers.

math.OC↗