# Quantized Primal-Dual Algorithms for Network Optimization with Linear Convergence

arXiv: https://arxiv.org/abs/2111.08180 · HTML: https://mlsanity.com/paper/2111.08180/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2021.

## Built on by (most cited first, 2 of 2)

- [Memory-Reduced Meta-Learning with Guaranteed Convergence](https://mlsanity.com/paper/2412.12030/) (2024)
- [Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry](https://mlsanity.com/paper/2606.11339/) (2026)

## Abstract (arXiv)

This paper studies the network optimization problem about which a group of agents cooperates to minimize a global function under practical constraints of finite bandwidth communication. Particularly, we propose an adaptive encoding-decoding scheme to handle the constrained communication between agents. Based on this scheme, the continuous-time quantized distributed primal-dual (QDPD) algorithm is developed for network optimization problems. We prove that our algorithms can exactly track an optimal solution to the corresponding convex global cost function at a linear convergence rate. Furthermore, we obtain the relation between communication bandwidth and the convergence rate of QDPD algorithms. Finally, an exponential regression example is given to illustrate our results.