# An Empirical Study of ADMM for Nonconvex Problems

arXiv: https://arxiv.org/abs/1612.03349 · HTML: https://mlsanity.com/paper/1612.03349/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2016.

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

- [Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM](https://mlsanity.com/paper/2407.13731/) (2024)

## Abstract (arXiv)

The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems. We provide an empirical study of the practical performance of ADMM on several nonconvex applications, including l0 regularized linear regression, l0 regularized image denoising, phase retrieval, and eigenvector computation. Our experiments suggest that ADMM performs well on a broad class of non-convex problems. Moreover, recently proposed adaptive ADMM methods, which automatically tune penalty parameters as the method runs, can improve algorithm efficiency and solution quality compared to ADMM with a non-tuned penalty.