TY - JOUR
T1 - A distributed ADMM-like method for resource sharing over time-varying networks
AU - Aybat, Necdet Serhat
AU - Hamedani, Erfan Yazdandoost
N1 - Publisher Copyright:
© 2019 Society for Industrial and Applied Mathematics
PY - 2019/12/12
Y1 - 2019/12/12
N2 - We consider cooperative multiagent resource sharing problems over time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific composite convex functions subject to a conic constraint that couples agents' decisions. We propose a distributed primal-dual algorithm, DPDA-D, to solve the saddle-point formulation of the sharing problem on time-varying (un)directed communication networks; and we show that the primal-dual iterate sequence converges to a point defined by a primal optimal solution and a consensual dual price for the coupling constraint. Furthermore, we provide convergence rates for suboptimality, infeasibility, and consensus violation of agents' dual price assessments; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithm; and compare DPDA-D with centralized methods on the basis pursuit denoising and multichannel power allocation problems.
AB - We consider cooperative multiagent resource sharing problems over time-varying communication networks, where only local communications are allowed. The objective is to minimize the sum of agent-specific composite convex functions subject to a conic constraint that couples agents' decisions. We propose a distributed primal-dual algorithm, DPDA-D, to solve the saddle-point formulation of the sharing problem on time-varying (un)directed communication networks; and we show that the primal-dual iterate sequence converges to a point defined by a primal optimal solution and a consensual dual price for the coupling constraint. Furthermore, we provide convergence rates for suboptimality, infeasibility, and consensus violation of agents' dual price assessments; examine the effect of underlying network topology on the convergence rates of the proposed decentralized algorithm; and compare DPDA-D with centralized methods on the basis pursuit denoising and multichannel power allocation problems.
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U2 - 10.1137/17m1151973
DO - 10.1137/17m1151973
M3 - Article
AN - SCOPUS:85084176261
SN - 1052-6234
VL - 29
SP - 3036
EP - 3068
JO - SIAM Journal on Optimization
JF - SIAM Journal on Optimization
IS - 4
ER -