Treelength of Series-parallel graphs

International audienceThe length of a tree-decompositionof a graph is the maximum distance between two vertices of a same bag of the decomposition. The treelength of a graph is the minimum length among its tree-decomposition. Treelength of graphs has been studied for its algorithmic applications in classical metric problems such as Traveling Salesman Problem or metric dimension of graphs and also, in compact routing in the context of distributed computing. Deciding whether the treelength of a general graph is at most 2 is NP-complete (graphs of treelength one are precisely the chordal graphs), and it is known that the treelength of agraph cannot be approximated up to a factor less than3/2 (the best known approximation algorithm for treelength has an approximation ratio of 3). However, nothing is known on the computational complexity of treelength in planar graphs, except that the treelength of any outerplanar graph is equal to the third of the maximum size of its isometric cycles. This work initiates the study of treelength in planar graphs by considering its next natural subclass, namely the one of series-parallel graphs. We first fully describe the treelength of melon graphs (set of pairwise internally disjointpaths linking two vertices), showing that, even in such a restricted graph class, the expression of the treelength is not trivial. Then, we show that treelength can be approximated up toa factor 3/2 in series-parallel graphs. Our main result is a polynomial-time algorithm for deciding whether a series-parallel graph has treelength at most 2. Our latter result relies on a characterization of series-parallel graphs with treelength 2 in terms of an infinite family of forbidden isometric subgraphs

Finding Optimal Tree Decompositions

The task of organizing a given graph into a structure called a tree decomposition is relevant in multiple areas of computer science. In particular, many NP-hard problems can be solved in polynomial time if a suitable tree decomposition of a graph describing the problem instance is given as a part of the input. This motivates the task of finding as good tree decompositions as possible, or ideally, optimal tree decompositions. This thesis is about finding optimal tree decompositions of graphs with respect to several notions of optimality. Each of the considered notions measures the quality of a tree decomposition in the context of an application. In particular, we consider a total of seven problems that are formulated as finding optimal tree decompositions: treewidth, minimum fill-in, generalized and fractional hypertreewidth, total table size, phylogenetic character compatibility, and treelength. For each of these problems we consider the BT algorithm of Bouchitté and Todinca as the method of finding optimal tree decompositions. The BT algorithm is well-known on the theoretical side, but to our knowledge the first time it was implemented was only recently for the 2nd Parameterized Algorithms and Computational Experiments Challenge (PACE 2017). The author’s implementation of the BT algorithm took the second place in the minimum fill-in track of PACE 2017. In this thesis we review and extend the BT algorithm and our implementation. In particular, we improve the eciency of the algorithm in terms of both theory and practice. We also implement the algorithm for each of the seven problems considered, introducing a novel adaptation of the algorithm for the maximum compatibility problem of phylogenetic characters. Our implementation outperforms alternative state-of-the-art approaches in terms of numbers of test instances solved on well-known benchmarks on minimum fill-in, generalized hypertreewidth, fractional hypertreewidth, total table size, and the maximum compatibility problem of phylogenetic characters. Furthermore, to our understanding the implementation is the first exact approach for the treelength problem

Optimal distance query reconstruction for graphs without long induced cycles

Let $G=(V,E)$ be an $n$-vertex connected graph of maximum degree $\Delta$. Given access to $V$ and an oracle that given two vertices $u,v\in V$, returns the shortest path distance between $u$ and $v$, how many queries are needed to reconstruct $E$? We give a simple deterministic algorithm to reconstruct trees using $\Delta n\log_\Delta n+(\Delta+2)n$ distance queries and show that even randomised algorithms need to use at least $\frac1{100} \Delta n\log_\Delta n$ queries in expectation. The best previous lower bound was an information-theoretic lower bound of $\Omega(n\log n/\log \log n)$. Our lower bound also extends to related query models including distance queries for phylogenetic trees, membership queries for learning partitions and path queries in directed trees. We extend our deterministic algorithm to reconstruct graphs without induced cycles of length at least $k$ using $O_{\Delta,k}(n\log n)$ queries, which includes various graph classes of interest such as chordal graphs, permutation graphs and AT-free graphs. Since the previously best known randomised algorithm for chordal graphs uses $O_{\Delta}(n\log^2 n)$ queries in expectation, we both get rid off the randomness and get the optimal dependency in $n$ for chordal graphs and various other graph classes. Finally, we build on an algorithm of Kannan, Mathieu, and Zhou [ICALP, 2015] to give a randomised algorithm for reconstructing graphs of treelength $k$ using $O_{\Delta,k}(n\log^2n)$ queries in expectation.Comment: 35 page

Complexity and Algorithms for ISOMETRIC PATH COVER on Chordal Graphs and Beyond

A path is isometric if it is a shortest path between its endpoints. In this article, we consider the graph covering problem Isometric Path Cover, where we want to cover all the vertices of the graph using a minimum-size set of isometric paths. Although this problem has been considered from a structural point of view (in particular, regarding applications to pursuit-evasion games), it is little studied from the algorithmic perspective. We consider Isometric Path Cover on chordal graphs, and show that the problem is NP-hard for this class. On the positive side, for chordal graphs, we design a 4-approximation algorithm and an FPT algorithm for the parameter solution size. The approximation algorithm is based on a reduction to the classic path covering problem on a suitable directed acyclic graph obtained from a breadth first search traversal of the graph. The approximation ratio of our algorithm is 3 for interval graphs and 2 for proper interval graphs. Moreover, we extend the analysis of our approximation algorithm to k-chordal graphs (graphs whose induced cycles have length at most k) by showing that it has an approximation ratio of k+7 for such graphs, and to graphs of treelength at most ?, where the approximation ratio is at most 6?+2

Structure vs métrique dans les graphes

International audienceL'émergence de réseaux de très grande taille oblige à repenser de nombreux problèmes sur les graphes : en apparence simples, mais pour lesquels les algorithmes de résolution connus ne passent plus a l'échelle. Une approche possible est de mieux comprendre les propriétés de ces réseaux complexes, et d'en déduire de nouvelles méthodes plus efficaces. C'est dans ce but que nous démontrons des relations générales entre les propriétés structurelles des graphes et leurs propriétés métriques. Nos relations se déduisent de nouvelles bornes serrées sur le diamètre des séparateurs minimaux dans un graphe. Plus précisément , nous prouvons que dans tout graphe G le diamètre d'un séparateur minimal S dans G est au plus (l(G)/2) · (|S| − 1), avec l(G) la plus grande taille d'un cycle isométrique dans G. Nos preuves reposent sur des propriétés de connexité dans les puissances d'un graphe. Une conséquence de nos résultats est que pour tout graphe G, sa longueur arborescente (treelength) est au plus l(G)/2 fois sa largeur arborescente (treewidth). En complément de cette relation, nous bornons la largeur arborescente par une fonction de la longueur arborescente et du genre du graphe. Cette borne se généralise à la famille des graphes qui excluent un apex-graph H comme mineur. Par conséquent , nous obtenons un algorithme très simple qui, étant donné un graphe excluant un apex-graph fixé comme mineur, calcule sa largeur arborescente en temps O(n²) et avec facteur d'approximation O(l(G))

To Approximate Treewidth, Use Treelength!

International audienceTree-likeness parameters have proven their utility in the design of efficient algorithms on graphs. In this paper, we relate the structural tree-likeness of graphs with their metric tree-likeness. To this end, we establish new upper-bounds on the diameter of minimal separators in graphs. We prove that in any graph G, the diameter of any minimal separator S in G is at most ⌊l(G)/2⌋ · (|S| − 1), with l(G) the length of a longest isometric cycle in G. Our result relies on algebraic methods and on the cycle basis of graphs. We improve our bound for the graphs admitting a distance preserving elimination ordering, for which we prove that any minimal separator S has diameter at most 2 · (|S| − 1). We use our results to prove that the treelength tl(G) of any graph G is at most ⌊l(G)/2⌋ times its treewidth tw(G). In addition, we prove that, for any graph G that excludes an apex graph H as a minor, tw(G) ≤ c_H · tl(G) for some constant c_H only depending on H. We refine this constant when G has bounded genus. Altogether, we obtain a simple O(l(G))-approximation algorithm for computing the treewidth of n-node apex-minor-free graphs in O(n^2)-time