学术报告

【online】A generalized alternating direction implicit method for consensus optimization: .......

发布人:发布时间: 2022-06-30

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题目:A generalized alternating direction implicit method for consensus optimization: application to distributed sparse logistic regression


报告人:张文星 副教授 (电子科技大学)


方式:腾讯会议 ID:515-812-200


时间:2022年07月07日 19:00-20:00


摘要:A large family of paradigmatic models arising in the area of image/signal processing, machine learning and statistics regression can be boiled down to consensus optimization problems. We handle the generic consensus optimization by reformulating it as a monotone inclusion problem. We extend the algorithmic framework of the Hermitian and skew-Hermitian splitting method for linear systems of equations to the monotone plus skew-symmetric circumstance. Under some mild conditions, the proposed algorithm converges globally and is favourable for tackling consensus optimization on distributed computing architecture. Numerical experiments on sparse logistic regression are implemented in two distributed fashions. Compared to some state-of-the-art methods such as the alternating direction method of multipliers and the stochastic variance reduced gradient, the novel method exhibits competitive and appealing performances, especially when its relaxation factor approaches to zero.

 

报告人简介:张文星,电子科技大学副教授,2012年博士毕业于南京大学数学系。2014-2015年在法国图卢兹大学从事博士后研究。主要研究兴趣为最优化理论与算法、变分不等式及应用。目前主持国家自然科学基金面上项目一项。在Math Comput, SIAM J Imaging Sci, Inverse Problems, J Sci Comput, IEEE系列等杂志发表多篇学术论文。

 

邀请人:杨俊锋 老师