# PANDA: A Dual Linearly Converging Method for Distributed Optimization over Time-Varying Undirected Graphs

arXiv: https://arxiv.org/abs/1803.08328 · HTML: https://mlsanity.com/paper/1803.08328/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks](https://mlsanity.com/paper/2106.04469/) (2021)
- [ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks](https://mlsanity.com/paper/2102.09234/) (2021)
- [Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization](https://mlsanity.com/paper/2104.02596/) (2021)
- [Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying Networks](https://mlsanity.com/paper/2112.10389/) (2021)

## 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.