# On the inconsistency of ℓ1-penalised sparse precision matrix estimation

arXiv: https://arxiv.org/abs/1603.02532 · HTML: https://mlsanity.com/paper/1603.02532/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [Learning Graphical Factor Models with Riemannian Optimization](https://mlsanity.com/paper/2210.11950/) (2022)
- [Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning](https://mlsanity.com/paper/2506.10628/) (2025)
- [Maximum likelihood thresholds of Gaussian graphical models and graphical lasso](https://mlsanity.com/paper/2312.03145/) (2023)
- [Clusterpath Gaussian Graphical Modeling](https://mlsanity.com/paper/2407.00644/) (2024)

## Abstract (arXiv)

Various ℓ1-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to situations where the penalty term will dominate the optimisation. In this paper, we explore the consistency of ℓ1-based methods for a class of sparse latent variable -like models, which are strongly motivated by several types of applications. We show that all ℓ1-based methods fail dramatically for models with nearly linear dependencies between the variables. We also study the consistency on models derived from real gene expression data and note that the assumptions needed for consistency never hold even for modest sized gene networks and ℓ1-based methods also become unreliable in practice for larger networks.