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Main Author: Zhu, Ya-Nan
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.00385
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author Zhu, Ya-Nan
author_facet Zhu, Ya-Nan
contents This work proposes an Accelerated Primal-Dual Fixed-Point (APDFP) method that employs Nesterov type acceleration to solve composite problems of the form min f(x) + g(Bx), where g is nonsmooth and B is a linear operator. The APDFP features fully decoupled iterations and can be regarded as a generalization of Nesterov's accelerated gradient in the setting where B can be a non-identity matrix. Theoretically, we improve the convergence rate of the partial primal-dual gap with respect to the Lipschitz constant of the gradient of f from O(1/k) to O(1/k^2). Numerical experiments on graph-guided logistic regression and CT image reconstruction are conducted to validate the correctness and demonstrate the efficiency of the proposed method.
format Preprint
id arxiv_https___arxiv_org_abs_2511_00385
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Accelerated primal dual fixed point algorithm
Zhu, Ya-Nan
Optimization and Control
90C25, 49M29, 65K05, 65Y20, 68U10
This work proposes an Accelerated Primal-Dual Fixed-Point (APDFP) method that employs Nesterov type acceleration to solve composite problems of the form min f(x) + g(Bx), where g is nonsmooth and B is a linear operator. The APDFP features fully decoupled iterations and can be regarded as a generalization of Nesterov's accelerated gradient in the setting where B can be a non-identity matrix. Theoretically, we improve the convergence rate of the partial primal-dual gap with respect to the Lipschitz constant of the gradient of f from O(1/k) to O(1/k^2). Numerical experiments on graph-guided logistic regression and CT image reconstruction are conducted to validate the correctness and demonstrate the efficiency of the proposed method.
title Accelerated primal dual fixed point algorithm
topic Optimization and Control
90C25, 49M29, 65K05, 65Y20, 68U10
url https://arxiv.org/abs/2511.00385