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Mathematics > Optimization and Control

arXiv:2104.01959 (math)
[Submitted on 5 Apr 2021 (v1), last revised 12 Apr 2021 (this version, v2)]

Title:Self-Healing First-Order Distributed Optimization

Authors:Israel L. Donato Ridgley, Randy A. Freeman, Kevin M. Lynch
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Abstract:In this paper we describe a parameterized family of first-order distributed optimization algorithms that enable a network of agents to collaboratively calculate a decision variable that minimizes the sum of cost functions at each agent. These algorithms are self-healing in that their correctness is guaranteed even if they are initialized randomly, agents drop in or out of the network, local cost functions change, or communication packets are dropped. Our algorithms are the first single-Laplacian methods to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods.
Comments: Corrected equation (40) by changing "min" to "max", results unaffected
Subjects: Optimization and Control (math.OC); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
Cite as: arXiv:2104.01959 [math.OC]
  (or arXiv:2104.01959v2 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.2104.01959
Journal reference: 2021 60th IEEE Conference on Decision and Control (CDC), Austin, TX, USA, 2021, pp. 3850-3856
Related DOI: https://doi.org/10.1109/CDC45484.2021.9683487

Submission history

From: Israel Donato Ridgley [view email]
[v1] Mon, 5 Apr 2021 15:20:02 UTC (134 KB)
[v2] Mon, 12 Apr 2021 16:01:49 UTC (134 KB)
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