Efficient and Accurate Graph Laplacian Estimation using Proximal Newton Method

The proposed proximal Newton method efficiently solves the nonconvex, Laplacian-constrained maximum likelihood estimation problem for learning sparse graph structures, outperforming existing methods in both accuracy and computational efficiency.