Ambros Gleixner, Sebastian Pokutta
Antonia Chmiela, Christoph Graczyck, Boro Sofranac, Christoph Spiegel
01.01.2021 − 31.12.2022
Modern Mixed Integer Linear Programming solvers are among the most complex algorithms implemented in software today. They commonly rely on a Branch-and-Cut approach augmented with a diverse set of auxiliary techniques. Their performance is highly dependent on a number of interdependent individual components. Using tools from Machine Learning, we intend to study the interaction of individual decisions made in these components with the ultimate goal to improve performance. The practical implementation of resulting algorithms will involve the transfer of classic MIP techniques onto modern hardware like GPUs.