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Distributed Optimization via Local Computation Algorithms

London, Palma and Chen, Niangjun and Vardi, Shai and Wierman, Adam (2017) Distributed Optimization via Local Computation Algorithms. ACM SIGMETRICS Performance Evaluation Review, 45 (2). pp. 30-32. ISSN 0163-5999. doi:10.1145/3152042.3152053.

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We propose a new approach for distributed optimization based on an emerging area of theoretical computer science -- local computation algorithms. The approach is fundamentally different from existing methodologies and provides a number of benefits, such as robustness to link failure and adaptivity in dynamic settings. Specifically, we develop an algorithm, LOCO, that given a convex optimization problem P with n variables and a "sparse" linear constraint matrix with m constraints, provably finds a solution as good as that of the best online algorithm for P using only O(log(n+m)) messages with high probability. The approach is not iterative and communication is restricted to a localized neighborhood. In addition to analytic results, we show numerically that the performance improvements over classical approaches for distributed optimization are significant, e.g., it uses orders of magnitude less communication than ADMM.

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Chen, Niangjun0000-0002-2289-9737
Additional Information:Copyright is held by author/owner(s).
Issue or Number:2
Record Number:CaltechAUTHORS:20180409-162519917
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85706
Deposited By: George Porter
Deposited On:10 Apr 2018 14:43
Last Modified:15 Nov 2021 20:31

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