  We propose a new scalable algorithm for holistic linear regression building
on Bertsimas & King (2016). Specifically, we develop new theory to model
significance and multicollinearity as lazy constraints rather than checking the
conditions iteratively. The resulting algorithm scales with the number of
samples $n$ in the 10,000s, compared to the low 100s in the previous framework.
Computational results on real and synthetic datasets show it greatly improves
from previous algorithms in accuracy, false detection rate, computational time
and scalability.
