S-ADDOPT: Decentralized Stochastic First-Order Optimization Over Directed Graphs
Abstract
In this letter, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed network of nodes. In contrast to the existing work, we use gradient tracking to improve certain aspects of the resulting algorithm. In particular, we propose the S-ADDOPT algorithm that assumes a stochastic first-order oracle at each node and show that for a constant...
Paper Details
Title
S-ADDOPT: Decentralized Stochastic First-Order Optimization Over Directed Graphs
Published Date
Jul 1, 2021
Journal
Volume
5
Issue
3
Pages
953 - 958
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History