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) < https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.


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install.packages("PRIMAL")

1.0.0 by Zichong Li, 2 months ago


Browse source code at https://github.com/cran/PRIMAL


Authors: Zichong Li , Qianli Shen


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Matrix

Linking to Rcpp, RcppEigen


See at CRAN