Decentralized double stochastic averaging gradient

By Aryan Mokhtari, Alejandro Ribeiro. 2015

11 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hanlin Tang
2. Ji Liu
3. Ce Zhang
4. Xiangru Lian
5. Ming Yan
6. Martin Jaggi
7. Sebastian U. Stich
8. Tong Zhang

In Computational Mechanics

1. Hanlin Tang
2. Ce Zhang
3. Ji Liu
4. Xiangru Lian
5. Ming Yan
6. Peter Richtárik
7. Tong Zhang
8. Wotao Yin

In Computer Networks and Communications

1. Hanlin Tang
2. Ji Liu
3. Ce Zhang
4. Xiangru Lian
5. Ming Yan
6. Jianyu Wang
7. Martin Jaggi
8. Tong Zhang

Citations in the corpus

• D2: Decentralized Training over Decentralized Data (2018, background): “Shi et al. [2014] and Mokhtari and Ribeiro [2015] analyzed the decentralized version of the ADMM optimization algorithm.”
• 2016 Ieee International Conference on Big Data (big Data) Consensus Optimization with Delayed and Stochastic Gradients on Decentralized Networks (unknown): “A correction technique is developed for decentralized gradient descent with convergence rate of O(1/T ) with constant step size in [11], which results in a saddle-point algorithm as pointed out in [13].”