Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition

TL;DR

This paper introduces a bridge-block decomposition method for efficiently learning large-scale MTP2 Gaussian graphical models, significantly reducing computational complexity and improving speed over existing algorithms.

Contribution

The paper proposes a novel bridge-block decomposition approach that simplifies large-scale MTP2 Gaussian graphical model learning into smaller problems with explicit solutions, enhancing efficiency.

Findings

Significant speed-up over state-of-the-art methods.

Effective decomposition reduces computational complexity.

Validated on synthetic and real-world data.

Abstract

This paper studies the problem of learning the large-scale Gaussian graphical models that are multivariate totally positive of order two ($\text{MTP}_2$). 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.