Convergence rate analysis of distributed gradient methods for smooth optimization

By Dusan Jakovetic, Joao Xavier, José M. F. Moura. 2012

8 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. Lie He
2. An Bian
3. Kun Yuan
4. Qing Ling
5. Martin Jaggi
6. Wotao Yin
7. Wei Shi
8. Gang Wu

In Computational Mechanics

1. Kun Yuan
2. Wotao Yin
3. Qing Ling
4. Wei Shi
5. An Bian
6. Lie He
7. Gang Wu
8. Martin Jaggi

In Computer Networks and Communications

1. Wotao Yin
2. Wei Shi
3. Kun Yuan
4. Qing Ling
5. Aryan Mokhtari
6. Gang Wu
7. Ali H. Sayed
8. Martin Jaggi

Citations in the corpus

• COLA: Decentralized Linear Learning (2018, methodology): “Decentralized gradient descent (DGD) with diminishing stepsizes was proposed by [Nedic and Ozdaglar, 2009, Jakovetic et al., 2012], showing convergence to the optimal solution at a sublinear rate.”
• On the Linear Convergence of the ADMM in Decentralized Consensus Optimization (2013, background): “Using a diminishing stepsize 1/k1/3 [30], the DGD shows sublinear convergence that is slow even for a complete graph (i.”