Decentralized Resource Allocation via Dual Consensus ADMM | IEEE Conference Publication | IEEE Xplore

Decentralized Resource Allocation via Dual Consensus ADMM


Abstract:

We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of ...Show More

Abstract:

We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of agent-specific convex objective functions, while the agents' decisions are coupled via a convex conic constraint. We derive two methods by applying the alternating direction method of multipliers (ADMM) for decentralized consensus optimization to the dual of our resource allocation problem. Both methods are fully parallelizable and decentralized in the sense that each agent exchanges information only with its neighbors in the network and requires only its own data for updating its decision. We prove convergence of the proposed methods and demonstrate their effectiveness with a numerical example.
Date of Conference: 10-12 July 2019
Date Added to IEEE Xplore: 29 August 2019
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Conference Location: Philadelphia, PA, USA

I. Introduction

Solving optimization problems in a distributed fashion has attracted increased attention in many research areas. This is mainly motivated by the rapid growth in size and complexity of modern datasets, which makes them hard (or even impossible) to process on a single computational unit [1]. On the other hand, optimization problems arising in multi-agent systems usually have a separable structure making distributed optimization methods a natural choice for solving them [2]. Even if such problems were solvable in a centralized fashion, the agents would need to share their local data and objective functions with the central coordinator, which would then raise information privacy issues [3].

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References

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