TY - GEN
T1 - Self-Healing First-Order Distributed Optimization
AU - Donato Ridgley, Israel L.
AU - Freeman, Randy A.
AU - Lynch, Kevin M.
N1 - Funding Information:
All authors are affiliated with Northwestern University, Evanston, IL 60208 USA (e-mail: israelridgley2023@u.northwestern.edu; freeman@northwestern.edu; kmlynch@northwestern.edu). 1Department of Electrical & Computer Engineering; 2Department of Mechanical Engineering; 3Northwestern Institute on Complex Systems; 4Center for Robotics and Biosystems This material is based upon work supported by the National Science Foundation under Grant No. CMMI-2024774.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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 convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods.
AB - 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 convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods.
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U2 - 10.1109/CDC45484.2021.9683487
DO - 10.1109/CDC45484.2021.9683487
M3 - Conference contribution
AN - SCOPUS:85126017563
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3850
EP - 3856
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
Y2 - 13 December 2021 through 17 December 2021
ER -