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20082026
most citedOn the Linear Convergence of the ADMM in Decentralized Consensus Optimization

873 citations · 5.4k across the 120 of their papers we have counts for

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Showing 2014 · math.OCShow all

6 papers · 2 filters

math.OC2014★ 34 cited

A globally convergent algorithm for nonconvex optimization based on block coordinate update

Yangyang Xu, Wotao Yin

Nonconvex optimization problems arise in many areas of computational science and engineering and are (approximately) solved by a variety of algorithms. Existing algorithms usually…

math.OC2014★ 5 cited

Block stochastic gradient iteration for convex and nonconvex optimization

Yangyang Xu, Wotao Yin

The stochastic gradient (SG) method can minimize an objective function composed of a large number of differentiable functions, or solve a stochastic optimization problem, to a mode…

math.OC2014★ 48 cited

Self Equivalence of the Alternating Direction Method of Multipliers

Ming Yan, Wotao Yin

The alternating direction method of multipliers (ADM or ADMM) breaks a complex optimization problem into much simpler subproblems. The ADM algorithms are typically short and easy t…

math.OC2014★ 36 cited

Faster convergence rates of relaxed Peaceman-Rachford and ADMM under regularity assumptions

Damek Davis, Wotao Yin

Splitting schemes are a class of powerful algorithms that solve complicated monotone inclusion and convex optimization problems that are built from many simpler pieces. They give r…

math.OC2014★ 9 cited

Convergence rate analysis of several splitting schemes

Damek Davis, Wotao Yin

Splitting schemes are a class of powerful algorithms that solve complicated monotone inclusions and convex optimization problems that are built from many simpler pieces. They give…

math.OC2014★ 11 cited

EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization

Wei Shi, Qing Ling, Gang Wu +1

Recently, there have been growing interests in solving consensus optimization problems in a multi-agent network. In this paper, we develop a decentralized algorithm for the consens…