Group sparsity via linear-time projection

We present an efficient spectral projected-gradient algorithm for optimization subject to a group one-norm constraint. Our approach is based on a novel linear-time algorithm for Euclidean projection onto the one- and group one-norm constraints. Numerical experiments on large data sets suggest that the proposed method is substantially more efficient and scalable than existing methods.


Technical Report TR-2008-09, Department of Computer Science, University of British Columbia, June 2008



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