What is the best route from point A to point B? Navigation apps offer a number of metrics for the best route: travelling distance, travelling time, fuel consumption. No matter which metric is used, when the total number of possible routes is large, obtaining an optimal route does not seem to be a trivial matter, at least not by hand.

Problems of optimizing the use of resources subject to constraints are all around us. Industrial applications include, but not limited to, nurse rostering, transit scheduling, supply chain management, facility location, telecommunication network design, portfolio optimization, timetabling. In this presentation, we will see how linear programming has led to impressive results in the solution of a wide range of optimization problems. We will also see how it has been used in settling a long-standing conjecture on sphere packing.

About the presenter:

Kevin Cheung is an Associate Professor at Carleton University in the School of Mathematics and Statistics. After completing his Ph.D. at the University of Waterloo in the Department of Combinatorics and Optimization under the supervision of William H. Cunningham in 2003, he spent two years at the Massachusetts Institute of Technology as an NSERC Postdoctoral Fellow under the mentorship of Michel X. Goemans. His current research interests are discrete optimization and computer-assisted proofs.

DeepAI article: https://deepai.org/machine-learning-glossary-and-terms/linear-programming