Fast algorithm for solving the sparse group lasso convex optimization problem. This package apply template metaprogramming techniques, therefore -- when compiling the package from source -- a high level of optimization is needed to gain full speed (e.g. for the GCC compiler use -O3). Use of multiple processors for cross validation and subsampling is supported through OpenMP. The Armadillo C++ library is used as the primary linear algebra engine. Armadillo is licensed under the MPL 2.0. The Armadillo C++ library is primarily developed at NICTA (Australia) by Conrad Sanderson, with contributions from around the world. Furthermore the package utilize various Boost libraries, in particular the Tuple library by Jaakko Jarvi and the Random library by Jens Maurer. The Boost libraries are licensed under the Boost Software License.
|Date of publication||2013-06-27 07:39:31|
|Maintainer||Martin Vincent <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|http://arxiv.org/abs/1205.1245 http://arma.sourceforge.net/, http://www.boost.org/|
create.sgldata: Create sgl data
prepare.args: Prepare sgl function arguments
rearrange.sgldata: Rearrange sgl.data
sgl.algorithm.config: Create a new algorithm configuration
sgl_cv: Sparse group lasso cross validation using multiple possessors
sgl_fit: Fit a sparse group lasso regularization path.
sgl_lambda_sequence: Generic routine for computing a lambda sequence for the...
sgl_predict: Sgl predict
sgl.standard.config: Standard algorithm configuration
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