Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction

Details Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction

2009-04-03

arxiv.org/abs/0904.0584v1

IEEE Signal Processing Letters, volume 16, issue 12, pages 1067 - 1070, 2009

Statistics

Machine Learning

10 pages, 3 figures

Scientific paper

10.1109/LSP.2009.2030111

We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because of the dual formulation. Moreover, the primal variable is explicitly updated and the sparsity in the solution is exploited. Numerical comparison with the state-of-the-art algorithms shows that the proposed algorithm is favorable when the design matrix is poorly conditioned or dense and very large.

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