l1_ls: Simple Matlab Solver for l1-regularized Least Squares Problems. l1_ls is a Matlab implementation of the interior-point method for ell_1-regularized least squares described in the paper: A Method for Large-Scale l1-Regularized Least Squares. l1_ls is developed for large problems. It can solve large sparse problems with a million variables with high accuracy in a few tens of minutes on a PC. It can also efficiently solve very large dense problems, that arise in sparse signal recovery with orthogonal transforms, by exploiting fast algorithms for these transforms.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving $\ell_1$-regularized least squares problems (2017)
- Wang, Yong; Zhou, Guanglu; Zhang, Xin; Liu, Wanquan; Caccetta, Louis: The non-convex sparse problem with nonnegative constraint for signal reconstruction (2016)
- Young, Sylvia; Goddard, Michael E.; Pryce, Jennie E.; Deng, Guang: Kernel methods and haplotypes used in selection of sparse DNA markers for protein yield in dairy cattle (2013)
- Rigollet, Philippe; Tsybakov, Alexandre: Exponential screening and optimal rates of sparse estimation (2011)