Nick Sahinidis — ALAMO: Machine learning from data and first principles

Seminar | October 2 | 3:30-5 p.m. | 3108 Etcheverry Hall

 Nick Sahinidis, Carnegie Mellon University

 Industrial Engineering & Operations Research

We have developed the ALAMO methodology with the aim of producing a tool capable of using data to learn algebraic models that are accurate and as simple as possible. ALAMO relies on (a) integer nonlinear optimization to build low-complexity models from input-output data, (b) derivative-free optimization to collect additional data points
that can be used to improve tentative models, and (c) global optimization to enforce physical constraints on the mathematical structure of the model. We present computational results and comparisons between ALAMO and a variety of learning techniques, including Latin hypercube sampling, simple least-squares regression, and the lasso. We also describe results from applications in CO 2 capture that motivated the development of ALAMO.

Nick Sahinidis
Department of Chemical Engineering
Carnegie Mellon University