# Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

arXiv: https://arxiv.org/abs/2006.15261 · HTML: https://mlsanity.com/paper/2006.15261/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Optimal Approximation Rate of ReLU Networks in terms of Width and Depth](https://mlsanity.com/paper/2103.00502/) (2021)
- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [Beyond L1: Faster and Better Sparse Models with skglm](https://mlsanity.com/paper/2204.07826/) (2022)
- [L0Learn: A Scalable Package for Sparse Learning using L0 Regularization](https://mlsanity.com/paper/2202.04820/) (2022)
- [Slow Kill for Big Data Learning](https://mlsanity.com/paper/2305.01726/) (2023)

## Abstract (arXiv)

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies. Besides, the library allows users to choose different sparsity-inducing regularizers, including the convex ℓ1, nonconvex MCP and SCAD regularizers. The library is coded in C++ and has user-friendly R and Python wrappers. Numerical experiments demonstrate that picasso can scale up to large problems efficiently.