Lots of statistical procedures are based on an underlying optimization problem. Least squares regression and maximum likelihood estimation are two obvious examples. In a few cases, linear programming is used. Some examples are:

- Least absolute deviation (LAD) regression [1]
- Chebyshev regression [2]
- Quantile regression [3]

**sparse**vector \(\color{darkred}\beta\) from the linear model \[\color{darblue}y=\color{darkblue}X\color{darkred}\beta+\color{darkred}e\] where the number of observations \(n\) (rows in \(\color{darkblue}X\)) is (much) smaller than the number of coefficients \(p\) to estimate (columns in \(\color{darkblue}X\)) [4]: \(p \gg n\). This is an alternative to the well-known Lasso method [5].