The estimation of large sparse inverse covariance matrices is a ubiquitous statistical
problem in many application areas such as mathematical finance, geology, health, and …

The ℓ 1-regularized Gaussian maximum likelihood method is a common approach for
sparse precision matrix estimation, but one that poses a computational challenge for high …

We consider the problem of estimating a sparse Gaussian Graphical Model with a special
graph topological structure and more than a million variables. Most previous scalable …

We present SQUIC, a fast and scalable package for sparse precision matrix estimation. The
algorithm employs a second-order method to solve the-regularized maximum likelihood …

Learning graphs represented by-matrices via an-regularized Gaussian maximum-likelihood
method is a popular approach, but also one that poses computational challenges for large …

We GPU ported with CUDA the solver module of the Astrometric Verification Unit–Global
Sphere Reconstruction (AVU–GSR) pipeline for the ESA Gaia mission. The code finds the …

Fundamental tasks in multivariate and numerical analysis, such as sparse precision matrix
estimation via graphical lasso and function approximation, are formulated in ever-increasing …

A central machine is interested in estimating the underlying structure of a sparse Gaussian
Graphical Model (GGM) from a dataset distributed across multiple local machines. The local …

Estimating the graphical structures of high dimensional data and identifying the presence of
clusters in them are ubiquitous tasks in every scientific domain that deals with interacting or …

Computacionais)–Instituto de Matemática e Estatística, Universidade do Estado do Rio de
Janeiro, Rio de Janeiro. Sistemas lineares são modelos recorrentes na resolução de …