Algorithm 1042: Sparse Precision Matrix Estimation with SQUIC
Additional information
Authors
Eftekhari A.,
Gaedke-Merzhäuser L.,
Pasadakis D.,
Bollhöfer M.,
Scheidegger S.,
Schenk O.
Type
Journal Article
Year
2024
Language
English
Abstract
We present
SQUIC
, a fast and scalable package for sparse precision matrix estimation. The algorithm employs a second-order method to solve the
\(\ell_{1}\)
-regularized maximum likelihood problem, utilizing highly optimized linear algebra subroutines. In comparative tests using synthetic datasets, we demonstrate that
SQUIC
not only scales to datasets of up to a million random variables but also consistently delivers runtimes that are significantly faster than other well-established sparse precision matrix estimation packages. Furthermore, we showcase the application of the introduced package in classifying microarray gene expressions. We demonstrate that by utilizing a matrix form of the tuning parameter (also known as the regularization parameter),
SQUIC
can effectively incorporate prior information into the estimation procedure, resulting in improved application results with minimal computational overhead.
Journal
ACM Transactions on Mathematical Software
Volume
50
Number ( Month )
2
Pages (or article number)
1-18
ISSN
0098-3500, 1557-7295
DOI
Diffusion
License
License undefined
Visibility
Private
