pub fn l1_regression_lp(x: &Matrix, y: &[f64]) -> Result<Vec<f64>, GeomError>Expand description
Least-absolute-deviations regression, solved as a linear program.
Minimises sum |y_i - x_i . beta| by splitting each residual into a
positive and a negative part. The result is far less sensitive to an
outlier than a least-squares fit, because the cost of a large residual
grows linearly rather than quadratically – an outlier at ten standard
deviations pulls a hundred times harder on a least-squares fit than on
this one.
x holds one row per observation. Add a column of ones for an intercept.
§Errors
Returns an error on a shape mismatch or if the program has no optimum.