Handbook of Big Data Analytics pp 425-453 | Cite as

# A Tutorial on Open image in new window: R Package for the Linearized Bregman Algorithm in High-Dimensional Statistics

## Abstract

The R package, Open image in new window , stands for the LInearized BRegman Algorithm in high-dimensional statistics. The Linearized Bregman Algorithm is a simple iterative procedure which generates sparse regularization paths of model estimation. This algorithm was firstly proposed in applied mathematics for image restoration, and is particularly suitable for parallel implementation in large-scale problems. The limit of such an algorithm is a sparsity-restricted gradient descent flow, called the Inverse Scale Space, evolving along a parsimonious path of sparse models from the null model to overfitting ones. In sparse linear regression, the dynamics with early stopping regularization can provably meet the unbiased oracle estimator under nearly the same condition as LASSO, while the latter is biased. Despite its successful applications, proving the consistency of such dynamical algorithms remains largely open except for some recent progress on linear regression. In this tutorial, algorithmic implementations in the package are discussed for several widely used sparse models in statistics, including linear regression, logistic regression, and several graphical models (Gaussian, Ising, and Potts). Besides the simulation examples, various applications are demonstrated, with real-world datasets such as diabetes, publications of COPSS award winners, as well as social networks of two Chinese classic novels, Journey to the West and Dream of the Red Chamber.

## Keywords

Linearized Bregman iteration LASSO Variable selection Regularization path## Notes

### Acknowledgements

The authors would like to thank Chendi Huang, Stanley J. Osher, Ming Yan, and Wotao Yin for helpful discussions. The research of Jiechao Xiong and Yuan Yao was supported in part by National Basic Research Program of China: 2015CB85600 and 2012CB825501, National Natural Science Foundation of China: 61370004 and 11421110001 (A3 project), as well as grants from Baidu and Microsoft Research Asia. The research of Feng Ruan was partially supported by the E.K. Potter Stanford Graduate Fellowship.

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