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Auteurs principaux: Huang, Yushen, Sun, Yifan
Format: Preprint
Publié: 2025
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Accès en ligne:https://arxiv.org/abs/2501.08146
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author Huang, Yushen
Sun, Yifan
author_facet Huang, Yushen
Sun, Yifan
contents We investigate a family of approximate multi-step proximal point methods, framed as implicit linear discretizations of gradient flow. The resulting methods are multi-step proximal point methods, with similar computational cost in each update as the proximal point method. We explore several optimization methods where applying an approximate multistep proximal points method results in improved convergence behavior. We also include convergence analysis for the proposed method in several problem settings: quadratic problems, general problems that are strongly or weakly (non)convex, and accelerated results for alternating projections.
format Preprint
id arxiv_https___arxiv_org_abs_2501_08146
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Proximal Flow Inspired Multi-Step Methods
Huang, Yushen
Sun, Yifan
Optimization and Control
We investigate a family of approximate multi-step proximal point methods, framed as implicit linear discretizations of gradient flow. The resulting methods are multi-step proximal point methods, with similar computational cost in each update as the proximal point method. We explore several optimization methods where applying an approximate multistep proximal points method results in improved convergence behavior. We also include convergence analysis for the proposed method in several problem settings: quadratic problems, general problems that are strongly or weakly (non)convex, and accelerated results for alternating projections.
title Proximal Flow Inspired Multi-Step Methods
topic Optimization and Control
url https://arxiv.org/abs/2501.08146