Accelerated Distributed Nesterov Gradient Descent for Smooth and Strongly Convex Functions

被引：0

作者：

Qu, Guannan ^{[1
]}

Li, Na ^{[1
]}

机构：

[1] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA

来源：

2016 54TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON) | 2016年

关键词：

CONSENSUS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers the distributed optimization problem over a network, where the objective is to optimize a global function formed by a sum of local functions, using only local computation and communication. We develop an Accelerated Distributed Nesterov Gradient Descent (Acc-DNGD) method for strongly-convex and smooth functions. We show that it achieves a linear convergence rate and analyze how the convergence rate depends on the condition number and the underlying graph structure.

引用

页码：209 / 216

页数：8

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