The FASTCLIME package for linear programming and large-scale precision matrix estimation in R. We develop an R package FASTCLIME for solving a family of regularized linear programming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example, one use of our LP solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained L 1 Minimization Estimator). Compared with existing packages for this problem such as CLIME and FLARE, our package has three advantages: (1) it efficiently calculates the full piecewise- linear regularization path; (2) it provides an accurate dual certificate as stopping criterion; (3) it is completely coded in C and is highly portable. This package is designed to be useful to statisticians and machine learning researchers for solving a wide range of problems.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Wang, Beilun; Singh, Ritambhara; Qi, Yanjun: A constrained $\ell1$ minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models (2017)
- Cai, T.Tony; Ren, Zhao; Zhou, Harrison H.: Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation (2016)
- Lee, Wonyul; Liu, Yufeng: Joint estimation of multiple precision matrices with common structures (2015)
- Ren, Zhao; Sun, Tingni; Zhang, Cun-Hui; Zhou, Harrison H.: Asymptotic normality and optimalities in estimation of large Gaussian graphical models (2015)
- Pang, Haotian; Liu, Han; Vanderbei, Robert: The FASTCLIME package for linear programming and large-scale precision matrix estimation in R (2014)