TY - GEN
T1 - Distributed Learning with Convex SUM-of -Non-Convex Objective
AU - Zhang, Mengfei
AU - Chen, Jie
AU - Richard, Cedric
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Recent research works have shown that some classical optimization methods originally designed for dealing with convex problems can demonstrate similar properties when applied to non-convex scenar-ios, where the problems are both locally and globally non-convex. This has led to the widespread development of distributed strategies for non-convex problems. When the sum of the local non-convex costs remains (strongly) convex and the individual local costs are smooth, it indicates a specific scenario that has notable applications and can benefit from existing solving methods. In this paper, drawing inspiration from the efficiency and stability of diffusion adaptation, we explore the minimization of a strongly convex sum of non-convex local costs. Specifically, we provide an analysis to demonstrate the convergence behavior of the network. Simulations are conducted to validate our theory.
AB - Recent research works have shown that some classical optimization methods originally designed for dealing with convex problems can demonstrate similar properties when applied to non-convex scenar-ios, where the problems are both locally and globally non-convex. This has led to the widespread development of distributed strategies for non-convex problems. When the sum of the local non-convex costs remains (strongly) convex and the individual local costs are smooth, it indicates a specific scenario that has notable applications and can benefit from existing solving methods. In this paper, drawing inspiration from the efficiency and stability of diffusion adaptation, we explore the minimization of a strongly convex sum of non-convex local costs. Specifically, we provide an analysis to demonstrate the convergence behavior of the network. Simulations are conducted to validate our theory.
KW - diffusion adaptation
KW - non-convex cost
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=85184993236&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP58249.2023.10403519
DO - 10.1109/CAMSAP58249.2023.10403519
M3 - 会议稿件
AN - SCOPUS:85184993236
T3 - 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
SP - 36
EP - 40
BT - 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Y2 - 10 December 2023 through 13 December 2023
ER -