A PTAS for Bounded-Capacity Vehicle Routing in Planar Graphs

The Capacitated Vehicle Routing problem is to find a minimum-cost set of tours that collectively cover clients in a graph, such that each tour starts and ends at a specified depot and is subject to a capacity bound on the number of clients it can serve. In this paper, we present a polynomial-time approximation scheme (PTAS) for instances in which the input graph is planar and the capacity is bounded. Previously, only a quasipolynomial-time approximation scheme was known for these instances. To obtain this result, we show how to embed planar graphs into bounded-treewidth graphs while preserving, in expectation, the client-to-client distances up to a small additive error proportional to client distances to the depot

Travelling on Graphs with Small Highway Dimension

We study the Travelling Salesperson (TSP) and the Steiner Tree problem (STP) in graphs of low highway dimension. This graph parameter was introduced by Abraham et al. [SODA 2010] as a model for transportation networks, on which TSP and STP naturally occur for various applications in logistics. It was previously shown [Feldmann et al. ICALP 2015] that these problems admit a quasi-polynomial time approximation scheme (QPTAS) on graphs of constant highway dimension. We demonstrate that a significant improvement is possible in the special case when the highway dimension is 1, for which we present a fully-polynomial time approximation scheme (FPTAS). We also prove that STP is weakly NP-hard for these restricted graphs. For TSP we show NP-hardness for graphs of highway dimension 6, which answers an open problem posed in [Feldmann et al. ICALP 2015]

A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms

Parameterization and approximation are two popular ways of coping with NP-hard problems. More recently, the two have also been combined to derive many interesting results. We survey developments in the area both from the algorithmic and hardness perspectives, with emphasis on new techniques and potential future research directions

Hierarchy of Transportation Network Parameters and Hardness Results

The graph parameters highway dimension and skeleton dimension were introduced to capture the properties of transportation networks. As many important optimization problems like Travelling Salesperson, Steiner Tree or k-Center arise in such networks, it is worthwhile to study them on graphs of bounded highway or skeleton dimension. We investigate the relationships between mentioned parameters and how they are related to other important graph parameters that have been applied successfully to various optimization problems. We show that the skeleton dimension is incomparable to any of the parameters distance to linear forest, bandwidth, treewidth and highway dimension and hence, it is worthwhile to study mentioned problems also on graphs of bounded skeleton dimension. Moreover, we prove that the skeleton dimension is upper bounded by the max leaf number and that for any graph on at least three vertices there are edge weights such that both parameters are equal. Then we show that computing the highway dimension according to most recent definition is NP-hard, which answers an open question stated by Feldmann et al. [Andreas Emil Feldmann et al., 2015]. Finally we prove that on graphs G=(V,E) of skeleton dimension O(log^2 |V|) it is NP-hard to approximate the k-Center problem within a factor less than 2

On Sparse Hitting Sets: From Fair Vertex Cover to Highway Dimension

We consider the Sparse Hitting Set (Sparse-HS) problem, where we are given a set system (V,?,?) with two families ?,? of subsets of the universe V. The task is to find a hitting set for ? that minimizes the maximum number of elements in any of the sets of ?. This generalizes several problems that have been studied in the literature. Our focus is on determining the complexity of some of these special cases of Sparse-HS with respect to the sparseness k, which is the optimum number of hitting set elements in any set of ? (i.e., the value of the objective function). For the Sparse Vertex Cover (Sparse-VC) problem, the universe is given by the vertex set V of a graph, and ? is its edge set. We prove NP-hardness for sparseness k ? 2 and polynomial time solvability for k = 1. We also provide a polynomial-time 2-approximation algorithm for any k. A special case of Sparse-VC is Fair Vertex Cover (Fair-VC), where the family ? is given by vertex neighbourhoods. For this problem it was open whether it is FPT (or even XP) parameterized by the sparseness k. We answer this question in the negative, by proving NP-hardness for constant k. We also provide a polynomial-time (2-1/k)-approximation algorithm for Fair-VC, which is better than any approximation algorithm possible for Sparse-VC or the Vertex Cover problem (under the Unique Games Conjecture). We then switch to a different set of problems derived from Sparse-HS related to the highway dimension, which is a graph parameter modelling transportation networks. In recent years a growing literature has shown interesting algorithms for graphs of low highway dimension. To exploit the structure of such graphs, most of them compute solutions to the r-Shortest Path Cover (r-SPC) problem, where r > 0, ? contains all shortest paths of length between r and 2r, and ? contains all balls of radius 2r. It is known that there is an XP algorithm that computes solutions to r-SPC of sparseness at most h if the input graph has highway dimension h. However it was not known whether a corresponding FPT algorithm exists as well. We prove that r-SPC and also the related r-Highway Dimension (r-HD) problem, which can be used to formally define the highway dimension of a graph, are both W[1]-hard. Furthermore, by the result of Abraham et al. [ICALP 2011] there is a polynomial-time O(log k)-approximation algorithm for r-HD, but for r-SPC such an algorithm is not known. We prove that r-SPC admits a polynomial-time O(log n)-approximation algorithm

Algoritmy pro grafy malé highway dimension

V této práci navrhneme algoritmy pro problém k-Supplier with Outliers. V síti dostaneme zadanou množinu dodavatelů a množinu klientů. Cílem je vybrat k doda- vatelů tak, aby vzdálenost mezi každým obslouženým klientem a nejbližším vybraným dodavatelem byla co nejmenší. Je dovoleno ponechat některé klienty neobsloužené. Max- imální počet klientů, které nemusíme obsloužit, je dán na vstupu. Jelikož k-Supplier with Outliers má mnoho využití v logistice, soustředíme se na parametry, které jsou vhodné pro dopravní sítě. Zabýváme se grafy s malou highway dimension, která byla zavedena Abrahamem et al. [SODA 2010] a grafy s malou doubling dimension. Je známo, že za předpokladu P ̸= NP nelze pro žádné kladné ε problém k-Sup- plier with Outliers (3 − ε)-aproximovat. Problém k-Supplier with Outliers je W[1]-těžký pro grafy s konstantní doubling dimension a highway dimension. Oba tyto těžkostní výsledky překonáme pomocí paradigmatu parametrizovaných aproximačních algoritmů. V případě highway dimension navrhneme (1 + ε)-aproximační algoritmus pro jakéko- liv kladné ε pracující v čase f(k, p, h, ε) · nO(1) , kde p je povolený počet klientů, které nemusíme obsloužit, h je highway dimension grafu na vstupu a f je nějaká vyčíslitelná funkce. V případě doubling dimension navrhneme (1 + ε)-aproximační algoritmus pro...In this work we develop algorithms for the k-Supplier with Outliers problem. In a network, we are given a set of suppliers and a set of clients. The goal is to choose k suppliers so that the distance between every served client and its nearest supplier is minimized. Clients that are not served are called outliers and the number of allowed outliers is given on input. As k-Supplier with Outliers has numerous applications in logistics, we focus on parameters which are suitable for transportation networks. We study graphs with low highway dimension, which was proposed by Abraham et al. [SODA 2010], and low doubling dimension. It is known that unless P = NP, k-Supplier with Outliers does not admit a (3 − ε)-approximation algorithm for any constant ε > 0. The k-Supplier with Outliers problem is W[1]-hard on graphs of constant doubling dimension for parame- ters k and highway dimension. We overcome both of these barriers through the paradigm of parameterized approximation algorithms. In the case of highway dimension, we develop a (1 + ε)-approximation algorithm for any ε > 0 with running time f(k, p, h, ε) · nO(1) where p is the number of allowed outliers, h is the highway dimension of the input graph, and f is some computable function. In the case of doubling dimension, we develop a (1 + ε)-approximation...Department of Applied MathematicsKatedra aplikované matematikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic