R package picasso: Pathwise Calibrated Sparse Shooting Algorithm. Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Ge, Jason; Li, Xingguo; Jiang, Haoming; Liu, Han; Zhang, Tong; Wang, Mengdi; Zhao, Tuo: \textttpicasso: a sparse learning library for high dimensional data analysis in \textttRand \textttPython (2019)
- Li, Xingguo; Zhao, Tuo; Arora, Raman; Liu, Han; Hong, Mingyi: On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization (2018)
- Yang, Zhuoran; Ning, Yang; Liu, Han: On semiparametric exponential family graphical models (2018)
- Yaohui Zeng, Patrick Breheny: The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R (2017) arXiv