Scalable Holistic Linear Regression

DOI10.1016/J.ORL.2020.02.008zbMATH Open1525.62031arXiv1902.03272MaRDI QIDQ6313821

Michael Lingzhi Li, Dimitris Bertsimas

Publication date: 8 February 2019

Abstract: 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.

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Mixed integer programming (90C11)

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