Multivariate Algorithmics for Graph and String Problems in Bioinformatics

目前解决生物信息学中出现的NP-Hard问题的最好方法是将数据约简与整数线性规划(ILP)相结合。参数化算法也促进了这些问题的高效算法的发展。MAGZ研究项目旨在促进从参数算法到多元算法的演变。这将导致识别和利用生物数据的不同结构特征的算法。其目的是建立多变量算法作为解决生物信息学中NP-Hard问题的ILPS的同等强大的替代方案。最终，这将显著增加可解实例的范围。

The current best approach to solving NP-hard problems arising inbioinformatics is the combination of data reduction and formulation asinteger linear program (ILP). Parameterized algorithmics has alsocontributed to the development of efficient algorithms for theseproblems. The research project MAGZ aims to promote the evolution fromparameterized to multivariate algorithmics. This shall result inalgorithms that identify and exploit different structural features ofbiological data. The aim is to establish multivariate algorithmics asan equally powerful alternative to ILPs for solving NP-hard problemsin bioinformatics. Eventually, this shall significantly increase therange of solvable instances.