Content:
Machine learning methods and optimization methods can be combined in manifold ways. In this seminar, we concentrate on the use of machine learning techniques to accelerate already existing optimization algorithms, in particular solvers for mixed-integer (linear) programs.
Which subroutines of well-known optimization algorithms can profit from an application machine learning? Which machine learning algorithms are best suited to support decision making within optimization algorithms? Which problem classes benefit from combined approaches? Those question have been studied and partially answered in the literature of the past five years. Some highlights of recent research results are reviewed and we gain some insight into the current developments in this field.