DESTRESS: Computation-optimal and communication-efficient decentralized nonconvex finite-sum optimization
Emerging applications in multiagent environments such as internet-of-things, networked sensing, autonomous systems, and federated learning, call for decentralized algorithms for finite-sum optimizations that are resource efficient in terms of both computation and communication. In this paper, we con...
Saved in:
Main Authors: | LI, Boyue, LI, Zhize, CHI, Yuejie |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/8691 https://ink.library.smu.edu.sg/context/sis_research/article/9694/viewcontent/SIMODS22_destress.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Simple and optimal stochastic gradient methods for nonsmooth nonconvex optimization
by: LI, Zhize, et al.
Published: (2022) -
A gradient-free distributed optimization method for convex sum of nonconvex cost functions
by: Pang, Yipeng, et al.
Published: (2023) -
BEER: Fast O(1/T) rate for decentralized nonconvex optimization with communication compression
by: ZHAO, Haoyu, et al.
Published: (2022) -
STOCHASTIC BREGMAN GRADIENT-TYPE ALGORITHMS FOR NONCONVEX OPTIMIZATION AND CONVEX-CONCAVE SADDLE POINT PROBLEMS
by: DING KUANGYU
Published: (2023) -
Robust optimization for unconstrained simulation-based problems
by: Bertsimas, D., et al.
Published: (2014)