SearcharxivSearch

arXiv subjects

Tarek Stuck

Publications and source records attributed to Tarek Stuck.

2 recordsLinked to original sources

Finding Regions of Maximum Circularity in Plane Geometric Graphs

A problem that occurs in different applications in geographical information science is to generate compact regions from areas on a map. This is important, e.g., in the context of electoral districting to avoid gerrymandering. A common measure for the compactness of a region is the Polsby-Popper score, which measures how close a given region is to a circle based on its area and perimeter. We assume that a polygonal subdivision of the plane is given and study the problem of selecting a subset of the polygonal faces that maximizes the Polsby-Popper score, given by $\frac{4\pi A}{P^2}$, where $A$ is the area of the selected shape and $P$ is its perimeter. We consider the more general task of maximizing $\frac{A}{P^\alpha}$ for an arbitrary $\alpha>1$, which we call the $\alpha$-circularity problem. We perform the first rigorous study of its complexity and show that it is weakly NP-hard if $\alpha \in (1,2]$. Furthermore, for $\alpha>1$ we present a pseudopolynomial time algorithm for this problem.

cs.DS

Parameterized Algorithms for Computing Pareto Sets

Dynamic programming over tree decompositions is a common technique in parameterized algorithms. In this paper, we study whether this technique can also be applied to compute Pareto sets of multiobjective optimization problems. We first derive an algorithm to compute the Pareto set for the multicriteria s-t cut problem and show how this result can be applied to a polygon aggregation problem arising in cartography that has recently been introduced by Rottmann et al. (GIScience 2021). We also show how to apply these techniques to also compute the Pareto set of the multiobjective minimum spanning tree problem and for the multiobjective TSP. The running time of our algorithms is $O(f(w)\cdot\mathrm{poly}(n,p_{\text{max}}))$, where $f$ is some function in the treewidth $w$, $n$ is the input size, and $p_{\text{max}}$ is an upper bound on the size of the Pareto sets of the subproblems that occur in the dynamic program. Finally, we present an experimental evaluation of computing Pareto sets on real-world instances of polygon aggregation problems. For this matter we devised a task-specific data structure that allows for efficient storage and modification of large sets of Pareto-optimal solutions. Throughout the implementation process, we incorporated several improved strategies and heuristics that significantly reduced both runtime and memory usage, enabling us to solve instances with treewidth of up to 22 within reasonable amount of time. Moreover, we conducted a preprocessing study to compare different tree decompositions in terms of their estimated overall runtime.

cs.DS