# Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization

arXiv: https://arxiv.org/abs/2001.05537 · HTML: https://mlsanity.com/paper/2001.05537/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

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

- [On the Complexity of a Practical Primal-Dual Coordinate Method](https://mlsanity.com/paper/2201.07684/) (2022)
- [Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization](https://mlsanity.com/paper/2305.17323/) (2023)

## Abstract (arXiv)

Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e.g., sparsity) efficiently. In this paper, we propose a novel accelerated dual-averaging primal-dual algorithm for minimizing a composite convex function. We also derive a stochastic version of the proposed method which solves empirical risk minimization, and its advantages on handling sparse data are demonstrated both theoretically and empirically.