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Efficient Proximal Gradient Algorithms for Joint Graphical Lasso
- Source :
- Entropy, Entropy, Vol 23, Iss 1623, p 1623 (2021), Entropy; Volume 23; Issue 12; Pages: 1623
- Publication Year :
- 2021
- Publisher :
- MDPI, 2021.
-
Abstract
- We consider learning an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction method of multipliers (ADMM) is the main approach taken concerning for joint graphical lasso (JGL). We propose proximal gradient procedures with and without a backtracking option for the JGL. These procedures are first-order and relatively simple, and the subproblems are solved efficiently in closed form. We further show the boundedness for the solution of the JGL problem and the iterations in the algorithms. The numerical results indicate that the proposed algorithms can achieve high accuracy and precision, and their efficiency is competitive with state-of-the-art algorithms.<br />23 pages, 5 figures
- Subjects :
- FOS: Computer and information sciences
Computer science
Science
QC1-999
General Physics and Astronomy
Machine Learning (stat.ML)
Astrophysics
Article
Lasso (statistics)
Simple (abstract algebra)
Statistics - Machine Learning
FOS: Mathematics
Graphical model
Mathematics - Optimization and Control
Sparse matrix
Efficient algorithm
Backtracking
Physics
joint graphical lasso
QB460-466
Gaussian graphical model
proximal gradient descent method
Optimization and Control (math.OC)
Iterated function
Joint (audio engineering)
Algorithm
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 23
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- Entropy
- Accession number :
- edsair.doi.dedup.....c505b80bb1e37cfadc161cf1b3448fe6