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The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation in R.

Haotian Pang1, Han Liu, Robert Vanderbei2

  • 1Department of Electrical Engineering, Princeton University, Olden St Princeton, NJ 08540, USA.

Journal of Machine Learning Research : JMLR
|January 27, 2015
PubMed
Summary
This summary is machine-generated.

We introduce fastclime, an R package for regularized linear programming (LP). It offers efficient solutions for large-scale problems, including sparse precision matrix estimation using the Constrained L1 Minimization Estimator (CLIME) method.

Keywords:
high dimensional datalinear programmingparametric simplex methodsparse precision matrixundirected graphical model

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Area of Science:

  • Computational Statistics
  • Machine Learning
  • Optimization

Background:

  • Linear programming (LP) problems are fundamental in optimization.
  • Sparse precision matrix estimation is crucial for high-dimensional data analysis.
  • Existing R packages for CLIME have limitations in efficiency and feature set.

Purpose of the Study:

  • To develop a novel R package, fastclime, for solving regularized linear programming problems.
  • To provide an efficient and scalable tool for sparse precision matrix estimation.
  • To offer advantages over existing packages in calculating regularization paths and utilizing dual certificates.

Main Methods:

  • Implementation of the parametric simplex algorithm for LP.
  • Development of the fastclime R package, coded entirely in C for portability and speed.
  • Utilizing a dual certificate as an accurate stopping criterion.

Main Results:

  • The fastclime package efficiently calculates the full piecewise-linear regularization path for LP problems.
  • It provides an accurate dual certificate, serving as a reliable stopping criterion.
  • The package demonstrates superior performance and portability compared to existing CLIME implementations.

Conclusions:

  • fastclime is a powerful and efficient R package for solving regularized linear programming problems.
  • It significantly enhances the capability for sparse precision matrix estimation.
  • The package is valuable for statisticians and machine learning researchers dealing with large-scale optimization problems.