PRIMAL: 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) <>.

Package details

AuthorZichong Li, Qianli Shen
MaintainerZichong Li <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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PRIMAL documentation built on Jan. 22, 2020, 5:06 p.m.