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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

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