Local reoptimization for turbocharging heuristics

迄今为止，理论计算机科学对有效启发式的设计几乎没有影响。虽然数据集可能很大，但重要的结构几乎总是存在，并且在设计算法时必须考虑到这一点。参数化复杂性不仅通过参数化输入的大小，而且通过参数化结构参数来考虑底层结构。该项目旨在利用新理论在设计新启发式和对计算困难问题的现有启发式进行涡轮增压方面的许多机会。

Theoretical computer science has up until now had little impact on the design of effective heuristics. While data sets may be large, significant structure is almost always present and important to take into account when designing algorithms. Parameterised complexity considers the underlying structure by parameterising not only on the size of the input but also on structural parameters. This project aims to take advantage of the many opportunities for new theories in the design of new heuristics and in turbocharging existing heuristics for computationally hard problems.