Proximal Gradient Algorithms Under Local Lipschitz Gradient Continuity: A Convergence and Robustness Analysis of PANOC

Alberto De Marchi, Andreas Themelis

Research output: Contribution to journalArticlepeer-review

Abstract

Composite optimization offers a powerful modeling tool for a variety of applications and is often numerically solved by means of proximal gradient methods. In this paper, we consider fully nonconvex composite problems under only local Lipschitz gradient continuity for the smooth part of the objective function. We investigate an adaptive scheme for PANOC-type methods (Stella et al. in Proceedings of the IEEE 56th CDC, 2017), namely accelerated linesearch algorithms requiring only the simple oracle of proximal gradient. While including the classical proximal gradient method, our theoretical results cover a broader class of algorithms and provide convergence guarantees for accelerated methods with possibly inexact computation of the proximal mapping. These findings have also significant practical impact, as they widen scope and performance of existing, and possibly future, general purpose optimization software that invoke PANOC as inner solver.

Original languageEnglish
JournalJournal of Optimization Theory and Applications
DOIs
Publication statusAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics

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