Parametric Simplex Method for Sparse Learning

Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <>.


Reference manual

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1.0.2 by Zichong Li, a year ago

Browse source code at

Authors: Zichong Li , Qianli Shen

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Matrix

Linking to Rcpp, RcppEigen

See at CRAN