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.

References in zbMATH (referenced in 12 articles )

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  1. Kashlak, Adam B.: Non-asymptotic error controlled sparse high dimensional precision matrix estimation (2021)
  2. Wang, Cheng; Jiang, Binyan: An efficient ADMM algorithm for high dimensional precision matrix estimation via penalized quadratic loss (2020)
  3. Choi, Young-Geun; Lim, Johan; Choi, Sujung: High-dimensional Markowitz portfolio optimization problem: empirical comparison of covariance matrix estimators (2019)
  4. Liu, Jianyu; Yu, Guan; Liu, Yufeng: Graph-based sparse linear discriminant analysis for high-dimensional classification (2019)
  5. Fan, Jianqing; Liu, Han; Wang, Weichen: Large covariance estimation through elliptical factor models (2018)
  6. Karl Sjöstrand; Line Clemmensen; Rasmus Larsen; Gudmundur Einarsson; Bjarne Ersbøll: SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling (2018) not zbMATH
  7. Zhu, Yinchu; Bradic, Jelena: Significance testing in non-sparse high-dimensional linear models (2018)
  8. Wang, Beilun; Singh, Ritambhara; Qi, Yanjun: A constrained (\ell1) minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models (2017)
  9. Cai, T. Tony; Ren, Zhao; Zhou, Harrison H.: Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation (2016)
  10. Lee, Wonyul; Liu, Yufeng: Joint estimation of multiple precision matrices with common structures (2015)
  11. Ren, Zhao; Sun, Tingni; Zhang, Cun-Hui; Zhou, Harrison H.: Asymptotic normality and optimalities in estimation of large Gaussian graphical models (2015)
  12. Pang, Haotian; Liu, Han; Vanderbei, Robert: The FASTCLIME package for linear programming and large-scale precision matrix estimation in R (2014)