Informal presentation.

Informal presentation.

Most real-world optimization problems do not have an explicit problem formulation, but only allow to compute the quality of selected solution candidates. Solving such black-box optimization problems requires iterative procedures which use the feedback gained from previous evaluations to determine the strategy by which the next solution candidates are generated. Many black-box optimization algorithms, such as Simulated Annealing, Evolutionary Algorithms, Swarm Intelligence Algorithms, are randomized – making it very difficult to analyze their performances mathematically. In the last 15 years, the theory of randomized black-box optimization has advanced considerably, and has contributed to efficient optimization by providing insights into the working principles of black-box optimization which are hard or impossible to obtain by empirical means. On the other hand, empirically-guided benchmarking has opened up new research directions for theoretical investigations. In this presentation we will discuss the state of the art in the theory of randomized black-box optimization algorithms. As part of this critical survey we will also mention a number of open questions and connections to other fields of Computer Science.

Graph databases use graph structure to represent and query data. The talk will survey some graph query languages used in this context, with a focus on regular path queries (RPQs) and conjunctive regular path queries (CRPQs). We will present the different semantics that graph database systems use for them (every path, simple path, trail), and recall computational complexities of query evaluation and query containment. We will finally discuss some issues related to querying not only the topology but also the data of the graph and present a few open problems that could be of interest both to the graph and algorithms group and to the automata group.

This talk concerns the problem of quickly computing distances and shortest paths on distributed networks (the CONGEST model). There have been many developments for this problem in the last few year, resulting in tight approximation schemes. This left open whether exact algorithms can perform equally well. In this talk, we will discuss some recent progress in answering this question. Most recent works that this talk is based on are with Sebastian Krinninger (FOCS 2018) and Aaron Bernstein (ArXiv 2018).