# Stochastic Parallel Block Coordinate Descent for Large-scale Saddle Point Problems

arXiv: https://arxiv.org/abs/1511.07294 · HTML: https://mlsanity.com/paper/1511.07294/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2015.

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

- [Stability and Generalization for Randomized Coordinate Descent](https://mlsanity.com/paper/2108.07414/) (2021)

## Abstract (arXiv)

We consider convex-concave saddle point problems with a separable structure and non-strongly convex functions. We propose an efficient stochastic block coordinate descent method using adaptive primal-dual updates, which enables flexible parallel optimization for large-scale problems. Our method shares the efficiency and flexibility of block coordinate descent methods with the simplicity of primal-dual methods and utilizing the structure of the separable convex-concave saddle point problem. It is capable of solving a wide range of machine learning applications, including robust principal component analysis, Lasso, and feature selection by group Lasso, etc. Theoretically and empirically, we demonstrate significantly better performance than state-of-the-art methods in all these applications.