sglOptim: Generic Sparse Group Lasso Solver

Fast generic solver for sparse group lasso optimization problems. The loss (objective) function must be defined in a C++ module. Use of multiple processors for cross validation and subsampling is supported through OpenMP. The optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions.Convergence of the algorithm is established (see reference). Development version is on github.

Version: 1.2.0
Depends: R (≥ 3.0.0), Matrix
Imports: methods, stats, utils
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, BH
Published: 2015-09-19
Author: Martin Vincent
Maintainer: Martin Vincent <vincent at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: sglOptim citation info
Materials: NEWS
CRAN checks: sglOptim results


Reference manual: sglOptim.pdf
Package source: sglOptim_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: sglOptim_1.2.0.tgz, r-oldrel: sglOptim_1.0.122.1.tgz
OS X Mavericks binaries: r-release: sglOptim_1.2.0.tgz
Old sources: sglOptim archive

Reverse dependencies:

Reverse depends: lsgl, msgl
Reverse linking to: lsgl, msgl