TY - GEN
T1 - Distributed Optimization with Gradient Tracking Over Heterogeneous Delay-Prone Directed Networks
AU - Makridis, Evagoras
AU - Oliva, Gabriele
AU - Narahari, Kasagatta Ramesh
AU - Doostmohammadian, Mohammadreza
AU - Khan, Usman A.
AU - Charalambous, Themistoklis
N1 - Publisher Copyright:
© 2024 EUCA.
PY - 2024
Y1 - 2024
N2 - In this paper, we address the distributed optimization problem over unidirectional networks with possibly time-invariant heterogeneous bounded transmission delays. In particular, we propose a modified version of the Accelerated Distributed Directed OPTimization (ADD-OPT) algorithm, herein called Robustified ADD-OPT (R-ADD-OPT), which is able to solve the distributed optimization problem, even when the communication links suffer from heterogeneous but bounded transmission delays. We show that if the gradient step-size of the R-ADD-OPT algorithm is within a certain range, which also depends on the maximum time delay in the network, then the nodes are guaranteed to converge to the optimal solution of the distributed optimization problem. The range of the gradient step-size that guarantees convergence can be computed a priori based on the maximum time delay in the network.
AB - In this paper, we address the distributed optimization problem over unidirectional networks with possibly time-invariant heterogeneous bounded transmission delays. In particular, we propose a modified version of the Accelerated Distributed Directed OPTimization (ADD-OPT) algorithm, herein called Robustified ADD-OPT (R-ADD-OPT), which is able to solve the distributed optimization problem, even when the communication links suffer from heterogeneous but bounded transmission delays. We show that if the gradient step-size of the R-ADD-OPT algorithm is within a certain range, which also depends on the maximum time delay in the network, then the nodes are guaranteed to converge to the optimal solution of the distributed optimization problem. The range of the gradient step-size that guarantees convergence can be computed a priori based on the maximum time delay in the network.
KW - bounded delays
KW - directed graphs
KW - distributed optimization
KW - gradient tracking
KW - unidirectional networks
UR - http://www.scopus.com/inward/record.url?scp=85200549755&partnerID=8YFLogxK
U2 - 10.23919/ECC64448.2024.10590963
DO - 10.23919/ECC64448.2024.10590963
M3 - Conference article in proceedings
AN - SCOPUS:85200549755
T3 - 2024 European Control Conference, ECC 2024
SP - 2312
EP - 2319
BT - 2024 European Control Conference, ECC 2024
PB - IEEE
T2 - European Control Conference
Y2 - 25 June 2024 through 28 June 2024
ER -