Abstract. Modern algorithm theory includes numerous techniques to attack hard problems, such as approximation algorithms on the one hand and parameterized complexity on the other hand. However, it is still uncommon to use these two techniques simultaneously, which is unfortunate, as there are natural problems that cannot be solved using either technique alone, but rather well if we combine them. The problem to be studied here is not only natural, but also practical: Consider TSP, generalized as follows. We impose deadlines on some of the vertices, effectively constraining them to be visited prior to a given point of time. The resulting problem DLTSP (a special case of the well-known TSP with time windows) inherits its hardness from classical TSP, which is both well known from practice and renowned to be one of the hardest problems with respect to approximability: Within polynomial time, not even a polynomial approximation ratio (let alone a constant one) can be achieved (unless P = NP). We will show that DLTSP is even harder than classical TSP in the following sense. Classical TSP, despite its hardness, admits good approximation algorithms if restricted to metric (or near-metric) inputs. Not so DLTSP (and hence, neither TSP with time windows): We will prove that even for metric inputs, no constant approximation ratio can ever be achieved (unless P = NP). This is where parameterization becomes crucial: By combining methods from the field of approximation algorithms with ideas from the theory of parameterized complexity, we apply the concept of parameterized approximation. Thereby, we * This work was partially supported by SNF Grant 200021-109252/1. Joachim Kneis and Joachim Kupke were staying at ETH Zurich when this work was done. H.-J. Böckenhauer, J. Hromkovič, J. Kneis, and J. Kupke obtain a 2.5-approximation algorithm for DLTSP with a running time of k!· poly (|G|), where k denotes the number of deadlines. Furthermore, we prove that there is no fpt-algorithm with an approximation guarantee of 2 − ε for any ε > 0, unless P = NP. Finally, we show that, unlike TSP, DLTSP becomes much harder when relaxing the triangle inequality. More precisely, for an arbitrary small violation of the triangle inequality, DLTSP does not admit an fpt-algorithm with approximation guarantee ((1 − ε)/2)|V | for any ε > 0, unless P = NP.
For general metrics, REF showed that TSPTW does not admit a constant-factor approximation unless P = NP.
The Parameterized Approximability of TSP with Deadlines
Theory of Computing Systems
Theory of Computing Systems
Mathematics Computer Science