Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition
Abstract
This paper studies the problem of learning the large-scale Gaussian graphical models that are multivariate totally positive of order two (). By introducing the concept of bridge, which commonly exists in large-scale sparse graphs, we show that the entire problem can be equivalently optimized through (1) several smaller-scaled sub-problems induced by a \emph{bridge-block decomposition} on the thresholded sample covariance graph and (2) a set of explicit solutions on entries corresponding to bridges. From practical aspect, this simple and provable discipline can be applied to break down a large problem into small tractable ones, leading to enormous reduction on the computational complexity and substantial improvements for all existing algorithms. The synthetic and real-world experiments demonstrate that our proposed method presents a significant speed-up compared to the state-of-the-art benchmarks.
Cite
@article{arxiv.2309.13405,
title = {Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition},
author = {Xiwen Wang and Jiaxi Ying and Daniel P. Palomar},
journal= {arXiv preprint arXiv:2309.13405},
year = {2023}
}