Distributed Multiagent Convex Optimization Over Random Digraphs

Seyyed Shaho Alaviani

doi:10.1109/TAC.2019.2937499

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Distributed Multiagent Convex Optimization Over Random Digraphs

2020-03-01

Seyyed Shaho Alaviani *

Nicola Elia

DOI：10.1109/TAC.2019.2937499

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Abstract

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This paper considers an unconstrained collaborative optimization of a sum of convex functions, where agents make decisions using local information in the presence of random interconnection topologies. We recast the problem as minimization of the sum of convex functions over a constraint set defined as the set of fixed-value points of a random operator derived from weighted matrices of random graphs. We show that the derived random operator has nonexpansivity property; therefore, this formulation does not need the distribution of random communication topologies. Hence, it includes random networks with/without asynchronous protocols. As an extension of the problem, we define a novel optimization problem, namely minimization of a convex function over the fixed-value point set of a nonexpansive random operator. We propose a discrete-time algorithm using diminishing step size for converging almost surely and in mean square to the global solution of the optimization problem under suitable assumptions. Consequently, as a special case, it reduces to a totally asynchronous algorithm for the distributed optimization problem. We show that fixed-value point is a bridge from deterministic analysis to random analysis of the algorithm. Finally, a numerical example illustrates the convergence of the proposed algorithm.

Keywords:

Optimization

Convex functions

Convergence

Random variables

Hilbert space

Protocols

Minimization

Asynchronous

distributed convex optimization

fixed-value point

minimization over fixed-value point set

random graphs

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