ABSTRACT
In many industry settings, the same optimization problem is solved repeatedly for instances taken from a distribution that can be learned or forecasted. This talk studies how to speed up these parametric optimization problems to meet real-time constraints present in many applications. It reviews the concept of optimization proxies that learn the input/output mappings of parametric optimization problems, as well as novel techniques in “learning to optimize” for dimensionality reduction, The methodologies are highlighted on industrial problems in grid optimization, end-to-end supply chains, logistics, and transportation systems.
BIO
Pascal Van Hentenryck is the director of the NSF AI Institute for Advances in Optimization (AI4OPT) and a professor at Georgia Tech. He was a professor of Computer Science at Brown University for over 20 years and led the optimization research group at the National ICT Research in Australia. His current research focuses on AI for Engineering with applications in energy systems, supply chains and manufacturing, health care, corporate systems, and mobility.
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