An online optimization approach for multi-agent tracking of dynamic parameters in the presence of adversarial noise
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1702.06219.pdf
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Author(s)
Shahrampour, Shahin
Jadbabaie-Moghadam, Ali
Date Issued
May 2017
Journal
2017 American Control Conference (ACC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shahrampour, Shahin, and Ali Jadbabaie. “An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise.” 2017 American Control Conference (ACC) (May 2017).
Version
Author's final manuscript
Abstract
This paper addresses tracking of a moving target in a multi-agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent. Agents noisy observations could be nonlinear. We for- mulate this problem as a distributed online optimization where agents communicate with each other to track the minimizer of the global loss. We then propose a decentralized version of the Mirror Descent algorithm and provide the non-asymptotic analysis of the problem. Using the notion of dynamic regret, we measure the performance of our algorithm versus its offline counterpart in the centralized setting. We prove that the bound on dynamic regret scales inversely in the network spectral gap, and it represents the adversarial noise causing deviation with respect to the linear dynamics. Our result subsumes a number of results in the distributed optimization literature. Finally, in a numerical experiment, we verify that our algorithm can be simply implemented for multi-agent tracking with nonlinear observations.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.23919/ACC.2017.7963457
