A Coordinate-wise Optimization Algorithm for Sparse Inverse Covariance Selection

TL;DR

This paper introduces a coordinate-wise optimization algorithm for sparse inverse covariance selection, guaranteeing convergence to a coordinate-wise minimum and outperforming existing methods in accuracy on synthetic and real data.

Contribution

The paper presents a novel coordinate-wise optimization algorithm with convergence guarantees and a Newton-like method for solving reduced convex problems, improving accuracy over prior approaches.

Findings

Algorithm converges to a coordinate-wise minimum.

Outperforms existing solutions in accuracy.

Effective on both synthetic and real-world data.

Abstract

Sparse inverse covariance selection is a fundamental problem for analyzing dependencies in high dimensional data. However, such a problem is difficult to solve since it is NP-hard. Existing solutions are primarily based on convex approximation and iterative hard thresholding, which only lead to sub-optimal solutions. In this work, we propose a coordinate-wise optimization algorithm to solve this problem which is guaranteed to converge to a coordinate-wise minimum point. The algorithm iteratively and greedily selects one variable or swaps two variables to identify the support set, and then solves a reduced convex optimization problem over the support set to achieve the greatest descent. As a side contribution of this paper, we propose a Newton-like algorithm to solve the reduced convex sub-problem, which is proven to always converge to the optimal solution with global linear convergence rate and local quadratic convergence rate. Finally, we demonstrate the efficacy of our method on synthetic data and real-world data sets. As a result, the proposed method consistently outperforms existing solutions in terms of accuracy.