# DC Algorithm for Estimation of Sparse Gaussian Graphical Models

arXiv: https://arxiv.org/abs/2408.04206 · HTML: https://mlsanity.com/paper/2408.04206/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Structure Learning in Graphical Modeling](https://mlsanity.com/paper/1606.02359/) (2016)
- [Network Inference via the Time-Varying Graphical Lasso](https://mlsanity.com/paper/1703.01958/) (2017)
- [An Overview on the Estimation of Large Covariance and Precision Matrices](https://mlsanity.com/paper/1504.02995/) (2015)
- [DC approximation approaches for sparse optimization](https://mlsanity.com/paper/1407.0286/) (2014)
- [Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings](https://mlsanity.com/paper/1410.4726/) (2014)
- [Prediction of hierarchical time series using structured regularization and its application to artificial neural networks](https://mlsanity.com/paper/2007.15159/) (2020)
- [On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression](https://mlsanity.com/paper/2007.01169/) (2020)

## Abstract (arXiv)

Sparse estimation for Gaussian graphical models is a crucial technique for making the relationships among numerous observed variables more interpretable and quantifiable. Various methods have been proposed, including graphical lasso, which utilizes the ℓ1 norm as a regularization term, as well as methods employing non-convex regularization terms. However, most of these methods approximate the ℓ0 norm with convex functions. To estimate more accurate solutions, it is desirable to treat the ℓ0 norm directly as a regularization term. In this study, we formulate the sparse estimation problem for Gaussian graphical models using the ℓ0 norm and propose a method to solve this problem using the Difference of Convex functions Algorithm (DCA). Specifically, we convert the ℓ0 norm constraint into an equivalent largest-K norm constraint, reformulate the constrained problem into a penalized form, and solve it using the DC algorithm (DCA). Furthermore, we designed an algorithm that efficiently computes using graphical lasso. Experimental results with synthetic data show that our method yields results that are equivalent to or better than existing methods. Comparisons of model learning through cross-validation confirm that our method is particularly advantageous in selecting true edges.