Algorithmic Symmetry Detection and Exploitation

Symmetry is a ubiquitous concept that can both be a blessing and a curse. Symmetry arises naturally in many computational problems and can for example be used for search space compression or pruning. I will talk about algorithmic techniques to find symmetries and application scenarios that exploit them.

Starting with an introduction to the framework that has been established as the de facto standard over the past decades, the talk will highlight the underlying central ideas. I will then discuss several recent results and developments from the area. On the one hand, these results reassert the effectiveness of symmetry detection tools, but, on the other hand, they also show the limitations of the framework that is currently applied in practice. Finally, I will focus on how the central algorithmic ideas find their applications in areas such as machine learning and static program analysis.

Bio

Since 2014, Pascal Schweitzer is a junior-professor for the complexity of discrete problems at RWTH Aachen University. Following doctoral studies at the Max-Planck Institute for Computer Science in Saarbrücken, he was first a post-doctoral researcher at the Australian National University and then a laureate of the European Post-Doctoral Institute for Mathematical Sciences. His research interests comprise a wide range of discrete mathematics, including algorithmic and structural graph and group theory, on-line algorithms, and certifying algorithms.