# A Majorized ADMM with Indefinite Proximal Terms for Linearly Constrained Convex Composite Optimization

arXiv: https://arxiv.org/abs/1412.1911 · HTML: https://mlsanity.com/paper/1412.1911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2014.

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

- [Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals](https://mlsanity.com/paper/2404.02621/) (2024)

## Abstract (arXiv)

This paper presents a majorized alternating direction method of multipliers (ADMM) with indefinite proximal terms for solving linearly constrained 2-block convex composite optimization problems with each block in the objective being the sum of a non-smooth convex function and a smooth convex function, i.e., min_x ∈ cal X, ; y ∈ cal Yp(x)+f(x) + q(y)+g(y)mid A^* x+B^* y = c. By choosing the indefinite proximal terms properly, we establish the global convergence and O(1/k) ergodic iteration-complexity of the proposed method for the step-length τ ∈ (0, (1+√5)/2). The computational benefit of using indefinite proximal terms within the ADMM framework instead of the current requirement of positive semidefinite ones is also demonstrated numerically. This opens up a new way to improve the practical performance of the ADMM and related methods.