Self-Healing First-Order Distributed Optimization
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2021
- DOI:
- arXiv:
- arXiv:2104.01959
- Bibcode:
- 2021arXiv210401959D
- Keywords:
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- Mathematics - Optimization and Control;
- Computer Science - Multiagent Systems;
- Electrical Engineering and Systems Science - Systems and Control
- E-Print:
- Corrected equation (40) by changing "min" to "max", results unaffected