PANDA: A Dual Linearly Converging Method for Distributed Optimization over Time-Varying Undirected Graphs
Not on the map. Why?
Abstract (arXiv)
In this paper we consider a distributed convex optimization problem over time-varying networks. We propose a dual method that converges R-linearly to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. The proposed method requires half the amount of variable exchanges per iterate than methods based on DIGing, and yields improved practical performance as empirically demonstrated.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Mar 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4.
4 papers.
- 2021Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networkscited by 19
- 2021ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networkscited by 13
- 2021Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimizationcited by 11
- 2021Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying Networkscited by 1
- 2021Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying Networks20 Dec 2021
- 2021Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks8 Jun 2021
- 2021Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization6 Apr 2021
- 2021ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks18 Feb 2021