ON CONVERGENCE ANALYSIS OF GRADIENT BASED PRIMAL-DUAL METHOD OF MULTIPLIERS

被引：0
|
作者：
Zhang, Guoqiang [1 ]
O'Connor, Matthew [2 ]
Li, Le [3 ]
机构：
[1] Univ Technol Sydney, Sydney, NSW, Australia
[2] Australian Natl Univ, Canberra, ACT, Australia
[3] Northwestern Polytech Univ, Xian, Peoples R China
来源：
2018 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2018年
关键词：
Distributed optimization; ADMM; PDMM; convergence analysis; DISTRIBUTED OPTIMIZATION; ALGORITHMS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Recently, the primal-dual method of multipliers (PDMM) has been proposed and successfully applied to solve a number of decomposable convex optimizations distributedly and iteratively. In this work, we study the gradient based PDMM (GPDMM), where the objective functions are approximated using the gradient information per iteration. It is shown that for a certain class of decomposable convex optimizations, synchronous GPDMM has a sublinear convergence rate of O(1/K) (where K denotes the iteration index). Experiments on a problem of distributed ridge regularized logistic regression demonstrate the efficiency of synchronous GPDMM.
引用
收藏
页码：11 / 15
页数：5
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