TY - GEN
T1 - Heterogeneous Distributed Subgradient
AU - Lin, Yixuan
AU - Gamarra, Marco
AU - Liu, Ji
N1 - Publisher Copyright:
© 2024 AACC.
PY - 2024
Y1 - 2024
N2 - The paper proposes a heterogeneous push-sum based subgradient algorithm for multi-agent distributed convex optimization, in which each agent can arbitrarily switch between subgradient-push and push-subgradient at any time. It is shown that the heterogeneous algorithm converges to an optimal point at an optimal rate over time-varying directed graphs. The switching process within the heterogeneous algorithm can help prevent the leakage of agents' subgradient information.
AB - The paper proposes a heterogeneous push-sum based subgradient algorithm for multi-agent distributed convex optimization, in which each agent can arbitrarily switch between subgradient-push and push-subgradient at any time. It is shown that the heterogeneous algorithm converges to an optimal point at an optimal rate over time-varying directed graphs. The switching process within the heterogeneous algorithm can help prevent the leakage of agents' subgradient information.
UR - https://www.scopus.com/pages/publications/85204461808
U2 - 10.23919/ACC60939.2024.10645037
DO - 10.23919/ACC60939.2024.10645037
M3 - Conference contribution
AN - SCOPUS:85204461808
T3 - Proceedings of the American Control Conference
SP - 2809
EP - 2815
BT - 2024 American Control Conference, ACC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 American Control Conference, ACC 2024
Y2 - 10 July 2024 through 12 July 2024
ER -