ADMM Algorithm for Graphical Lasso with an ℓ_∞ Element-wise Norm Constraint

11/28/2013
by   Karthik Mohan, et al.
0

We consider the problem of Graphical lasso with an additional ℓ_∞ element-wise norm constraint on the precision matrix. This problem has applications in high-dimensional covariance decomposition such as in Janzamin-12. We propose an ADMM algorithm to solve this problem. We also use a continuation strategy on the penalty parameter to have a fast implemenation of the algorithm.

READ FULL TEXT

page 1

page 2

page 3

research
05/18/2012

Two New Algorithms for Solving Covariance Graphical Lasso Based on Coordinate Descent and ECM

Covariance graphical lasso applies a lasso penalty on the elements of th...
research
01/06/2021

Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation

We consider estimation of undirected Gaussian graphical models and inver...
research
09/26/2013

Bethe-ADMM for Tree Decomposition based Parallel MAP Inference

We consider the problem of maximum a posteriori (MAP) inference in discr...
research
11/10/2018

Anomaly Detection via Graphical Lasso

Anomalies and outliers are common in real-world data, and they can arise...
research
07/16/2021

Efficient proximal gradient algorithms for joint graphical lasso

We consider learning an undirected graphical model from sparse data. Whi...
research
03/07/2015

Exact Hybrid Covariance Thresholding for Joint Graphical Lasso

This paper considers the problem of estimating multiple related Gaussian...
research
06/04/2023

The Functional Graphical Lasso

We consider the problem of recovering conditional independence relations...

Please sign up or login with your details

Forgot password? Click here to reset