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Niklas Mohrin

Publications and source records attributed to Niklas Mohrin.

3 recordsLinked to original sources

Temporal Routing in Static Networks: The Schedule Completion Problem

We introduce the Temporally Edge Disjoint Schedule Completion (TEDSC) problem in which we need to cover a set of temporal edge demands $D$ by routing $k$ temporal walks through a directed static graph while remaining temporally edge disjoint. This problem combines the temporal aspects of train routing and passenger demands with the static nature of real-world rail networks. We show how to solve TEDSC in polynomial time. Motivated by real-world constraints, we next investigate two restricted variants of TEDSC in which each walk can travel only for some bounded distance or time $h$. For both variants, we present a $(2-h^{-1})$-approximation algorithm and fully characterize the parameterized landscape with respect to $k$, $h$, and $|D|$. Surprisingly, if we restrict the underlying train network, the two variants diverge: The distance variant stays $W[1]$-hard parameterized by $k$ even on a path of three vertices, whereas the time variant admits a polynomial-time algorithm on every fixed bidirected star graph.

cs.DS

Parameterized Complexity of Vehicle Routing

The Vehicle Routing Problem (VRP) is a popular generalization of the Traveling Salesperson Problem. Instead of one salesperson traversing the entire weighted, undirected graph $G$, there are $k$ vehicles available to jointly cover the set of clients $C \subseteq V(G)$. Every vehicle must start at one of the depot vertices $D \subseteq V(G)$ and return to its start. Capacitated Vehicle Routing (CVRP) additionally restricts the route of each vehicle by limiting the number of clients it can cover, the distance it can travel, or both. In this work, we study the complexity of VRP and the three variants of CVRP for several parameterizations, in particular focusing on the treewidth of $G$. We present an FPT algorithm for VRP parameterized by treewidth. For CVRP, we prove paraNP- and $W[\cdot]$-hardness for various parameterizations, including treewidth, thereby rendering the existence of FPT algorithms unlikely. In turn, we provide an XP algorithm for CVRP when parameterized by both treewidth and the vehicle capacity.

cs.CC

Behaviorally Correct Learning from Informants

In inductive inference, we investigate the learnability of classes of formal languages. We are interested in what classes of languages are learnable in certain learning settings. A class of languages is learnable, if there is a learner that can identify all of its languages and satisfies the constraints of the learning setting. To identify a language, a learner is presented with information about this very language. When learning from informants, this information consists of examples for numbers that are, and numbers that are not included in the target language. As more and more examples are presented, the learner outputs a hypothesis sequence. To satisfy behaviorally correct identification, this hypothesis sequence must eventually only list correct labels for the target language. In this thesis, we compare the effects of a number of semantic learning restrictions on the learning capabilities for behaviorally correct learning from informants.

cs.FL