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Proximal Gradient Algorithms Under Local Lipschitz Gradient Continuity

Author

Listed:
  • Alberto De Marchi

    (Universität der Bundeswehr München)

  • Andreas Themelis

    (Kyushu University)

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.

Suggested Citation

  • Alberto De Marchi & Andreas Themelis, 2022. "Proximal Gradient Algorithms Under Local Lipschitz Gradient Continuity," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 771-794, September.
    • Handle: RePEc:spr:joptap:v:194:y:2022:i:3:d:10.1007_s10957-022-02048-5
    DOI: 10.1007/s10957-022-02048-5
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    References listed on IDEAS

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    1. Haihao Lu & Robert M. Freund & Yurii Nesterov, 2018. "Relatively smooth convex optimization by first-order methods, and applications," LIDAM Reprints CORE 2965, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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