# Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems

arXiv: https://arxiv.org/abs/1705.03412 · HTML: https://mlsanity.com/paper/1705.03412/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2017.

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

- [Sign-regularized Multi-task Learning](https://mlsanity.com/paper/2102.11191/) (2021)
- [Generalized ADMM in Distributed Learning via Variational Inequality](https://mlsanity.com/paper/2104.12608/) (2021)

## Abstract (arXiv)

The classic Alternating Direction Method of Multipliers (ADMM) is a popular framework to solve linear-equality constrained problems. In this paper, we extend the ADMM naturally to nonlinear equality-constrained problems, called neADMM. The difficulty of neADMM is to solve nonconvex subproblems. We provide globally optimal solutions to them in two important applications. Experiments on synthetic and real-world datasets demonstrate excellent performance and scalability of our proposed neADMM over existing state-of-the-start methods.