# Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory

arXiv: https://arxiv.org/abs/1412.7477 · HTML: https://mlsanity.com/paper/1412.7477/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2014.

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

- [Continuation Path with Linear Convergence Rate](https://mlsanity.com/paper/2112.05104/) (2021)
- [Slow Kill for Big Data Learning](https://mlsanity.com/paper/2305.01726/) (2023)
- [Causal Discovery with Generalized Linear Models through Peeling Algorithms](https://mlsanity.com/paper/2310.16698/) (2023)
- [Quantum Algorithms for the Pathwise Lasso](https://mlsanity.com/paper/2312.14141/) (2023)
- [Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA](https://mlsanity.com/paper/2202.04719/) (2022)

## Abstract (arXiv)

The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: it warm start initialization, it active set updating, and it strong rule for coordinate preselection. Such a complex algorithmic structure grants superior empirical performance, but also poses significant challenge to theoretical analysis. To tackle this long lasting problem, we develop a new theory showing that these three features play pivotal roles in guaranteeing the outstanding statistical and computational performance of the pathwise coordinate optimization framework. Particularly, we analyze the existing pathwise coordinate optimization algorithms and provide new theoretical insights into them. The obtained insights further motivate the development of several modifications to improve the pathwise coordinate optimization framework, which guarantees linear convergence to a unique sparse local optimum with optimal statistical properties in parameter estimation and support recovery. This is the first result on the computational and statistical guarantees of the pathwise coordinate optimization framework in high dimensions. Thorough numerical experiments are provided to support our theory.