Large-Scale Precision Matrix Estimation With SQUIC
Additional information
Authors
Type
Journal Article
Year
2022
Language
English
Abstract
High-dimensional sparse precision matrix estimation is a ubiquitous task in multivariate analysis with applications that cross many disciplines. In this paper, we introduce the SQUIC package, which benefits from superior runtime performance and scalability, significantly exceeding the available state-of-the-art packages. This package is a second-order method that solves the L1--regularized maximum likelihood problem using highly optimized linear algebra subroutines, which leverage the underlying sparsity and the intrinsic parallelism in the computation. We provide two sets of numerical tests; the first one consists of didactic examples using synthetic datasets highlighting the performance and accuracy of the package, and the second one is a real-world classification problem of high dimensional medical datasets. The base algorithm is implemented in C++ with interfaces for R and Python.
Journal
Social Science Research Network
Start page number
1573
End page number
1375
Keywords
SQUIC, precision matrix, LASSO, maximum likelihood, R, Python
