# Primal-dual Accelerated Mirror-Descent Method for Constrained Bilinear Saddle-Point Problems

arXiv: https://arxiv.org/abs/2409.18285 · HTML: https://mlsanity.com/paper/2409.18285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Asymmetric regularization mechanism for GAN training with Variational Inequalities](https://mlsanity.com/paper/2601.13920/) (2026)

## Abstract (arXiv)

We develop a first-order accelerated algorithm for a class of constrained bilinear saddle-point problems with applications to network systems. The algorithm is a modified time-varying primal-dual version of an accelerated mirror-descent dynamics. It deals with constraints such as simplices and convex set constraints effectively, and converges with a rate of O(1/t2). Furthermore, we employ the acceleration scheme to constrained distributed optimization and bilinear zero-sum games, and obtain two variants of distributed accelerated algorithms.