Cedric Josz, Lexiao Lai, Xiaopeng Li
Published 2024-08-13Version 1
We study proximal random reshuffling for minimizing the sum of locally Lipschitz functions and a proper lower semicontinuous convex function without assuming coercivity or the existence of limit points. The algorithmic guarantees pertaining to near approximate stationarity rely on a new tracking lemma linking the iterates to trajectories of conservative fields. One of the novelties in the analysis consists in handling conservative fields with unbounded values.
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient
LIPO+: Frugal Global Optimization for Lipschitz Functions
$γ$-Competitiveness: An Approach to Multi-Objective Optimization with High Computation Costs in Lipschitz Functions
![QR code for arXiv:2408.07182 [math.OC]](http://oss.arxitics.com/images/favicon.svg)