A proximal point algorithm for log-determinant optimization with group Lasso regularization (Q2848177)
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scientific article; zbMATH DE number 6211565
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | A proximal point algorithm for log-determinant optimization with group Lasso regularization |
scientific article; zbMATH DE number 6211565 |
Statements
25 September 2013
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proximal point algorithm
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covariance selection
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log-determinant optimization
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group Lasso regularization
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augmented Lagrangian
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alternating direction method
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Newton's method
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Gaussian graphical model
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convergence
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numerical result
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A proximal point algorithm for log-determinant optimization with group Lasso regularization (English)
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The authors propose a proximal point algorithm for the solution of covariance selections problems, where it is assumed that the inverse covariance matrix has a block sparsity structure. In each iteration of the optimization algorithm the dual subproblem is used to update the primal variable. This approach is combined with an inexact Newton method to accelerate the optimization process. Global and local convergence results for the proposed method are proved. Furthermore, comprehensive numerical results are presented and discussed.
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