Optimization Online


ExtraPush for Convex Smooth Decentralized Optimization over Directed Networks

Jinshan Zeng(jinshanzeng***at***jxnu.edu.cn)
wotao Yin(wotaoyin***at***ucla.edu)

Abstract: In this note, we extend the existing algorithms Extra and subgradient-push to a new algorithm ExtraPush for convex consensus optimization over a directed network. When the network is stationary, we propose a simplified algorithm called Normalized ExtraPush. These algorithms use a fixed step size like in Extra and accept the column-stochastic mixing matrices like in subgradient-push. We present preliminary analysis for ExtraPush under a bounded sequence assumption. For Normalized ExtraPush, we show that it naturally produces a bounded, linearly convergent sequence provided that the objective function is strongly convex.

Keywords: Dencentralized optimization, directed graph, consensus, non-doubly stochastic, EXTRA

Category 1: Network Optimization

Category 2: Nonlinear Optimization

Citation: UCLA CAM Report 15-61, 2015

Download: [PDF]

Entry Submitted: 11/28/2015
Entry Accepted: 12/01/2015
Entry Last Modified: 11/28/2015

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society