Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Computational Optimization and Applications
Nonmonotone Spectral Projected Gradient Methods on Convex Sets
SIAM Journal on Optimization
Convex Optimization
Learning to Decode Cognitive States from Brain Images
Machine Learning
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Testing nonparametric and semiparametric hypotheses in vector stationary processes
Journal of Multivariate Analysis
The Journal of Machine Learning Research
Map approach to learning sparse Gaussian Markov networks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Smooth Optimization Approach for Sparse Covariance Selection
SIAM Journal on Optimization
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Adaptive First-Order Methods for General Sparse Inverse Covariance Selection
SIAM Journal on Matrix Analysis and Applications
Learning graphical models for stationary time series
IEEE Transactions on Signal Processing
A Bayesian approach to sparse dynamic network identification
Automatica (Journal of IFAC)
Stationary-sparse causality network learning
The Journal of Machine Learning Research
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An algorithm is presented for topology selection in graphical models of autoregressive Gaussian time series. The graph topology of the model represents the sparsity pattern of the inverse spectrum of the time series and characterizes conditional independence relations between the variables. The method proposed in the paper is based on an l1-type nonsmooth regularization of the conditional maximum likelihood estimation problem. We show that this reduces to a convex optimization problem and describe a large-scale algorithm that solves the dual problem via the gradient projection method. Results of experiments with randomly generated and real data sets are also included.