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.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Das, Abhimanyu; Kempe, David: Approximate submodularity and its applications: subset selection, sparse approximation and dictionary selection (2018)
- Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Vane, Zachary P.; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.: Compressive sensing with cross-validation and stop-sampling for sparse polynomial chaos expansions (2018)
- Peng, Yong; Kong, Wanzeng; Qin, Feiwei; Nie, Feiping: Manifold adaptive kernelized low-rank representation for semisupervised image classification (2018)
- Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving (\ell_1)-regularized least squares problems (2017)
- Vanderbei, Robert; Lin, Kevin; Liu, Han; Wang, Lie: Revisiting compressed sensing: exploiting the efficiency of simplex and sparsification methods (2016)
- Wang, Yong; Zhou, Guanglu; Zhang, Xin; Liu, Wanquan; Caccetta, Louis: The non-convex sparse problem with nonnegative constraint for signal reconstruction (2016)
- Peng, Yong; Lu, Bao-Liang; Wang, Suhang: Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning (2015)
- 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)