Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 1 of 1
Full-Text Articles in Engineering
Distributed Learning With Automated Stepsizes, Benjamin Liggett
Distributed Learning With Automated Stepsizes, Benjamin Liggett
All Theses
Stepsizes for optimization problems play a crucial role in algorithm convergence, where the stepsize must undergo tedious manual tuning to obtain near-optimal convergence. Recently, an adaptive method for automating stepsizes was proposed for centralized optimization. However, this method is not directly applicable to decentralized optimization because it allows for heterogeneous agent stepsizes. Furthermore, directly using consensus between agent stepsizes to mitigate stepsize heterogeneity can decrease performance and even lead to divergence.
This thesis proposes an algorithm to remedy the tedious manual tuning of stepsizes in decentralized optimization. Our proposed algorithm automates the stepsize and uses dynamic consensus between agents’ stepsizes …