Local reoptimization for turbocharging heuristics

到目前为止，理论计算机科学对有效的计算机设计几乎没有影响。虽然数据集可能很大，但重要的结构几乎总是存在的，并且在设计算法时需要考虑。参数化复杂性不仅考虑了输入的大小，还考虑了结构参数，从而考虑了底层结构。这个项目的目的是利用许多机会，新的理论在设计新的算法，并在涡轮增压现有的算法计算困难的问题。

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.