l1_ls

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.


References in zbMATH (referenced in 9 articles )

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  1. Das, Abhimanyu; Kempe, David: Approximate submodularity and its applications: subset selection, sparse approximation and dictionary selection (2018)
  2. 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)
  3. Peng, Yong; Kong, Wanzeng; Qin, Feiwei; Nie, Feiping: Manifold adaptive kernelized low-rank representation for semisupervised image classification (2018)
  4. Karimi, Sahar; Vavasis, Stephen: IMRO: A proximal quasi-Newton method for solving (\ell_1)-regularized least squares problems (2017)
  5. Vanderbei, Robert; Lin, Kevin; Liu, Han; Wang, Lie: Revisiting compressed sensing: exploiting the efficiency of simplex and sparsification methods (2016)
  6. Wang, Yong; Zhou, Guanglu; Zhang, Xin; Liu, Wanquan; Caccetta, Louis: The non-convex sparse problem with nonnegative constraint for signal reconstruction (2016)
  7. Peng, Yong; Lu, Bao-Liang; Wang, Suhang: Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning (2015)
  8. 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)
  9. Rigollet, Philippe; Tsybakov, Alexandre: Exponential screening and optimal rates of sparse estimation (2011)