# MIST: L0 Sparse Linear Regression with Momentum

arXiv: https://arxiv.org/abs/1409.7193 · HTML: https://mlsanity.com/paper/1409.7193/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2014.

## Built on by (most cited first, 1 of 1)

- [On Regularized Sparse Logistic Regression](https://mlsanity.com/paper/2309.05925/) (2023)

## Abstract (arXiv)

Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations. The penalty is responsible for inducing sparsity and the natural choice is the so-called l0 norm. In this paper we develop a Momentumized Iterative Shrinkage Thresholding (MIST) algorithm for minimizing the resulting non-convex criterion and prove its convergence to a local minimizer. Simulations on large data sets show superior performance of the proposed method to other methods.