Karen Aardal - Machine-learning augmented branch-and-bound for mixed-integer linear optimization скачать в хорошем качестве

Karen Aardal - Machine-learning augmented branch-and-bound for mixed-integer linear optimization

Part of Discrete Optimization Talks: https://talks.discreteopt.com Karen Aardal - TU Delft Speaker webpage: https://diamhomes.ewi.tudelft.nl/~kaa... Machine-learning augmented branch-and-bound for mixed-integer linear optimization Abstract: Mixed-integer linear optimization solvers use branch and bound as their main component. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm, such as primal heuristics, branching, cutting planes, node selection and solver configuration decisions. In this talk we mention a selection of results in this area together with some relations between integer linear optimization and deep learning. The talk is based on a joint paper with Lara Scavuzzo Montana, Andrea Lodi, and Neil Yorke-Smith. Biography: Karen Aardal holds a position of full professor of optimization at Delft University of Technology. Her research interests include integer optimization algorithms, approximation algorithms, applications of machine learning, and the modeling and resolution of problems within energy and health care. She is a former chair of the Mathematical Optimization Society, and has served on the board of the INFORMS Computing Society in three periods. She served on several editorial boards, such as Math Programming Series B, INFORMS J on Computing, and OR Letters. In the Netherlands she is serving on the board of domain Science of the Dutch Research Council NWO.