ORC IAP Seminar 2019 - Bartolomeo Stellato
ORC IAP Seminar 2019: Machine Learning and Operations Research
Bartolomeo Stellato "Bartolomeo Stellato"
http://orc.mit.edu/events/orc-iap-sem...
Abstract
We present a new way to see optimization problems. Using machine learning techniques we are able to predict the strategy behind the optimal solution in any continuous and mixed-integer convex optimization problem as a function of its key parameters. The benefits of our approach are interpretability and speed. We use interpretable machine learning algorithms such as optimal classification trees (OCTs) to gain insights on the relationship between the problem parameters and the optimal solution. In this way, optimization is no longer a black-box and we can understand it. In addition, once we train the predictor, we can solve optimization problems at very high speed. This aspect is also relevant for non interpretable machine learning methods such as neural networks (NNs) since they can be evaluated very efficiently after the training phase.
We show on several realistic examples that the accuracy behind our approach is in the 90%-100% range, while even when the predictions are not correct, the degree of suboptimality or infeasibility is very low. We also benchmark the computation time beating state of the art solvers by multiple orders of magnitude.
Therefore, our method provides on the one hand a novel insightful understanding of the optimal strategies to solve a broad class of continuous and mixed-integer optimization problems and on the other hand a powerful computational tool to solve online optimization at very high speed.
Bio
Bartolomeo Stellato is a Postdoctoral Associate at the Operations Research Center under the supervision of Prof. Dimitris Bertsimas. He obtained a D.Phil. (Ph.D.) in Engineering Science (2017) from the University of Oxford under the supervision of Prof. Paul Goulart as part of the Marie Curie EU project TEMPO. He received a B.Sc. degree in Automation Engineering (2012) from Politecnico di Milano and a M.Sc. in Robotics, Systems and Control (2014) from ETH Zürich. His research focuses on the interplay between machine learning and optimization. He is also interested in fast numerical methods for online optimization and optimal control. In 2016, he visited Prof. Stephen Boyd’s group at Stanford University where he developed the OSQP solver now widely used in academia and industry with tens of thousands of downloads per month. He is the recipient of the IEEE Transaction on Power Electronics 1st prize paper award.