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Bibliographic Details
Main Authors: Huang, Chengzhi, Chen, Jian, Tang, Liping
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2412.14007
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author Huang, Chengzhi
Chen, Jian
Tang, Liping
author_facet Huang, Chengzhi
Chen, Jian
Tang, Liping
contents This paper proposes a new backtracking strategy based on the FISTA accelerated algorithm for multiobjective optimization problems. The strategy focuses on solving the problem of Lipschitz constant being unknown. It allows estimate parameter updates non-increasingly. Furthermore, the proposed strategy effectively avoids the limitation in convergence proofs arising from the non-negativity of the auxiliary sequence, thus providing a theoretical guarantee for its performance. We demonstrate that, under relatively mild assumptions, the algorithm achieves the convergence rate of $O(1/k2)$.
format Preprint
id arxiv_https___arxiv_org_abs_2412_14007
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Accelerated Proximal Gradient Method with Backtracking for Multiobjective Optimization
Huang, Chengzhi
Chen, Jian
Tang, Liping
Optimization and Control
This paper proposes a new backtracking strategy based on the FISTA accelerated algorithm for multiobjective optimization problems. The strategy focuses on solving the problem of Lipschitz constant being unknown. It allows estimate parameter updates non-increasingly. Furthermore, the proposed strategy effectively avoids the limitation in convergence proofs arising from the non-negativity of the auxiliary sequence, thus providing a theoretical guarantee for its performance. We demonstrate that, under relatively mild assumptions, the algorithm achieves the convergence rate of $O(1/k2)$.
title Accelerated Proximal Gradient Method with Backtracking for Multiobjective Optimization
topic Optimization and Control
url https://arxiv.org/abs/2412.14007