New Frontiers for Parameterized Complexity (Research Project)
Many important computational problems are intractable on general instances. However, realistic problem instances often contain a certain hidden structure that can be exploited to solve the problem efficiently. The parameterized complexity paradigm provides the perfect tools and techniques to formalize, quantify and exploit various forms of structure in order to efficiently solve a plethora of NP-hard problems. This paradigm has been applied in many fundamental areas of computer science with great success, including graph algorithms, computational logic, boolean satisability, constraint satisfaction and others. However, there are prominent areas of computer science where we still lack a thorough understanding of the parameterized complexity of key computational problems.
This project aims at investigating the parameterized complexity of fundamental problems in two such high-impact areas: integer linear programming and machine learning. The obtained results will allow the design of efficient algorithms capable of exploiting various natural forms of structure in order to find exact solutions for NP-hard problems in these important areas of computer science. A secondary aim of the project is to apply newly obtained insights and advancements in other, including more traditional, frontiers of parameterized complexity research.