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Distributed Optimization With Local Domain: Applications in MPC and Network Flows

João Mota(joaomota***at***cmu.edu)
João Xavier(jxavier***at***isr.ist.utl.pt)
Pedro Aguiar(aguiar***at***isr.ist.utl.pt)
Markus Püschel(pueschel***at***inf.ethz.ch)

Abstract: In this paper we consider a network with P nodes, where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector x* minimizing the sum of all the functions. We make the additional assumption that the functions have intersecting local domains, i.e., each function depends only on some components of the variable. Consequently, each node is interested in knowing only some components of x*, not the entire vector. This allows for improvement in communication-efficiency. We apply our algorithm to model predictive control (MPC) and to network flow problems and show, through experiments on large networks, that our proposed algorithm requires less communications to converge than prior algorithms.

Keywords: Distributed algorithms, alternating direction method of multipliers (ADMM), Model Predictive Control, network flow, multicommodity flow, sensor networks

Category 1: Network Optimization

Category 2: Applications -- Science and Engineering (Control Applications )

Citation: submitted to IEEE Transactions on Automatic Control, May, 2013

Download: [PDF]

Entry Submitted: 06/05/2013
Entry Accepted: 06/05/2013
Entry Last Modified: 06/05/2013

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