@misc{Bley, author = {Bley, Andreas}, title = {On the Complexity of Vertex-Disjoint Length-Restricted Path Problems}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-3639}, number = {SC-98-20}, abstract = {Let \$G=(V,E)\$ be a simple graph and \$s\$ and \$t\$ be two distinct vertices of \$G\$. A path in \$G\$ is called \$\ell\$-bounded for some \$\ell\in\mathbb{N}\$, if it does not contain more than \$\ell\$ edges. We study the computational complexity of approximating the optimum value for two optimization problems of finding sets of vertex-disjoint \$\ell\$-bounded \$s,t\$-paths in \$G\$. First, we show that computing the maximum number of vertex-disjoint \$\ell\$-bounded \$s,t\$-paths is \$\mathcal{AP\kern-1pt X}\$--complete for any fixed length bound \$\ell\geq 5\$. Second, for a given number \$k\in\mathbb{N}\$, \$1\leq k \leq |V|-1\$, and non-negative weights on the edges of \$G\$, the problem of finding \$k\$ vertex-disjoint \$\ell\$-bounded \$s,t\$-paths with minimal total weight is proven to be \$\mathcal{NPO}\$--complete for any length bound \$\ell\geq 5\$. Furthermore, we show that, even if \$G\$ is complete, it is \$\mathcal{NP}\$--complete to approximate the optimal solution value of this problem within a factor of \$2^{\langle\phi\rangle^\epsilon}\$ for any constant \$0<\epsilon<1\$, where \$\langle\phi\rangle\$ denotes the encoding size of the given problem instance \$\phi\$. We prove that these results are tight in the sense that for lengths \$\ell\leq 4\$ both problems are polynomially solvable, assuming that the weights satisfy a generalized triangle inequality in the weighted problem. All results presented also hold for directed and non-simple graphs. For the analogous problems where the path length restriction is replaced by the condition that all paths must have length equal to \$\ell\$ or where vertex-disjointness is replaced by edge-disjointness we obtain similar results.}, language = {en} } @misc{Bley, author = {Bley, Andreas}, title = {On the Hardness of Finding Small Shortest Path Routing Conflicts}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-11276}, number = {09-15}, abstract = {Nowadays most data networks use shortest path protocols such as OSPF or IS-IS to route traffic. Given administrative routing lengths for the links of a network, all data packets are sent along shortest paths with respect to these lengths from their source to their destination. One of the most fundamental problems in planning shortest path networks is to decide whether a given set of routing paths forms a valid routing and, if this is not the case, to find a small subset of the given paths that cannot be shortest paths simultaneously for any routing lengths. In this paper we show that it is NP-hard to approximate the size of the smallest shortest path conflict by a factor less than 7/6.}, language = {en} } @misc{BodlaenderKoster, author = {Bodlaender, Hans L. and Koster, Arie M.C.A.}, title = {On the Maximum Cardinality Search Lower Bound for Treewidth}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-8201}, number = {04-45}, abstract = {The Maximum Cardinality Search algorithm visits the vertices of a graph in some order, such that at each step, an unvisited vertex that has the largest number of visited neighbors becomes visited. An MCS-ordering of a graph is an ordering of the vertices that can be generated by the Maximum Cardinality Search algorithm. The visited degree of a vertex \$v\$ in an MCS-ordering is the number of neighbors of \$v\$ that are before \$v\$ in the ordering. The visited degree of an MCS-ordering \$\psi\$ of \$G\$ is the maximum visited degree over all vertices \$v\$ in \$\psi\$. The maximum visited degree over all MCS-orderings of graph \$G\$ is called its {\em maximum visited degree}. Lucena (2003) showed that the treewidth of a graph \$G\$ is at least its maximum visited degree. We show that the maximum visited degree is of size \$O(\log n)\$ for planar graphs, and give examples of planar graphs \$G\$ with maximum visited degree \$k\$ with \$O(k!)\$ vertices, for all \$k\in \Bbb{N}\$. Given a graph \$G\$, it is NP-complete to determine if its maximum visited degree is at least \$k\$, for any fixed \$k\geq 7\$. Also, this problem does not have a polynomial time approximation algorithm with constant ratio, unless P=NP. Variants of the problem are also shown to be NP-complete. We also propose and experimentally analyses some heuristics for the problem. Several tiebreakers for the MCS algorithm are proposed and evaluated. We also give heuristics that give upper bounds on the value of the maximum visited degree of a graph, which appear to give results close to optimal on many graphs from real life applications.}, language = {en} } @misc{KrumkePoensgen, author = {Krumke, Sven and Poensgen, Diana}, title = {Online Call Admission in Optical Networks with Larger Wavelength Demands}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6890}, number = {02-22}, abstract = {In the problem of \emph{Online Call Admission in Optical Networks}, briefly called \textsc{oca}, we are given a graph \$G=(V,E)\$ together with a set of wavelengths~\$W\$ and a finite sequence \$\sigma=r_1,r_2,\dots\$ of calls which arrive in an online fashion. Each call~\$r_j\$ specifies a pair of nodes to be connected and an integral demand indicating the number of required lightpaths. A lightpath is a path in~\$G\$ together with a wavelength~\$\lambda \in W\$. Upon arrival of a call, an online algorithm must decide immediately and irrevocably whether to accept or to reject the call without any knowledge of calls which appear later in the sequence. If the call is accepted, the algorithm must provide the requested number of lightpaths to connect the specified nodes. The essential restriction is the wavelength conflict constraint: each wavelength is available only once per edge, which implies that two lightpaths sharing an edge must have different wavelengths. Each accepted call contributes a benefit equal to its demand to the overall profit. The objective in \textsc{oca} is to maximize the overall profit. Competitive algorithms for \textsc{oca} have been known for the special case where every call requests just a single lightpath. In this paper we present the first competitive online algorithms for the general case of larger demands.}, language = {en} } @phdthesis{Krumke, author = {Krumke, Sven}, title = {Online Optimization: Competitive Analysis and Beyond}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6925}, number = {02-25}, abstract = {Traditional optimization techniques assume, in general, knowledge of all data of a problem instance. There are many cases in practice, however, where decisions have to be made before complete information about the data is available. In fact, it may be necessary to produce a part of the problem solution as soon as a new piece of information becomes known. This is called an \emph{online situation}, and an algorithm is termed \emph{online}, if it makes a decision (computes a partial solution) whenever a new piece of data requests an action. \emph{Competitive analysis} has become a standard yardstick to measure the quality of online algorithms. One compares the solution produced by an online algorithm to that of an optimal (clairvoyant) offline algorithm. An online algorithm is called \$c\$-competitive if on every input the solution it produces has cost'' at most \$c\$~times that of the optimal offline algorithm. This situation can be imagined as a game between an online player and a malicious adversary. Although competitive analysis is a worst-case analysis and henceforth pessimistic, it often allows important insights into the problem structure. One can obtain an idea about what kind of strategies are promising for real-world systems and why. On the other hand there are also cases where the offline adversary is simply too powerful and allows only trivial competitiveness results. This phenomenon is called hitting the triviality barrier''. We investigate several online problems by means of competitive analysis. We also introduce new concepts to overcome the weaknesses of the standard approach and to go beyond the triviality barrier.}, language = {en} } @misc{Hiller, author = {Hiller, Benjamin}, title = {Probabilistic Competitive Analysis of a Dial-a-Ride Problem on Trees Under High Load}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-8896}, number = {05-56}, abstract = {In this paper we consider a simple variant of the Online Dial-a-Ride Problem from a probabilistic point of view. To this end, we look at a probabilistic version of this online Dial-a-Ride problem and introduce a probabilistic notion of the competitive ratio which states that an algorithm performs well on the vast majority of the instances. Our main result is that under the assumption of high load a certain online algorithm is probabilistically \$(1+o(1))\$-competitive if the underlying graph is a tree. This result can be extended to general graphs by using well-known approximation techniques at the expense of a distortion factor~\$O(\log\|V\|)\$.}, language = {en} }