README.md

In TriangularCell/PgaMsgl: Proximal Gradient Algorithm for Multi-variate Sparse Group Lasso

PgaMsgl

PgaMsgl is an R package on Proximal Gradient Algorithm for Multi-variate Sparse Group Lasso. It is mainly implemented with Rcpp and RcppEigen.

Installing

Download the whole package and Build within R.

OR

Use install from the devtools package and run

install("directory of PgaMsgl", dependencies = TRUE)

OR

Use command install.packages if the dependicies have been installed on your devices.

install.packages("/path_to_PgaMsgl/PgaMsgl_version.tar.gz", repos=NULL, type="sourse", INSTALL_opts=c('--no-lock'))

Later on it will be released on CRAN.

Running the tests

The package includes two demo datasets for testing usage. One is with relative low dimension, and another is with relative high dimension.

For example, to run the low dimension dataset:

data(lowD)
system.time(lowD_result <- PgaMsgl(lowD$X, lowD$Y, lowD$B0, model="L121", lowD$Gm, lowD$mi, lowD$mg, lowD$mc))

Authors

Yiming Qin - Initial work - TriangularCell

License

This project is licensed under the GPL-3.0 License - see the LICENSE.md file for details

Acknowledgments

• Yaohua Hu
• Jing Qin
• Xinlin Hu
• Sifan Liu

References

Hu, Yaohua, et al. "Group sparse optimization via lp, q regularization." J Mach Learn Res 18 (2017): 1-52.

Beck, Amir, and Marc Teboulle. "A fast iterative shrinkage-thresholding algorithm for linear inverse problems." SIAM journal on imaging sciences 2.1 (2009): 183-202.

TriangularCell/PgaMsgl documentation built on May 28, 2019, 9:33 a.m.