We review the concepts of hypertree decomposition and hypertree width from a graph theoretical perspective and report on a number of recent results related to these concepts. We also showas a new result -that computing hypertree decompositions is fixed-parameter intractable. Hypertree Decompositions: Definition and BasicsThis paper reports about the recently introduced concept of hypertree decomposition and the associated notion of hypertree width. The latter is a cyclicity measure for hypergraphs, and constitutes a hypergraph invariant as it is preserved under hypergraph isomorphisms. Many interesting NP-hard problems are polynomially solvable for classes of instances are associated with hypergraphs of bounded width. This is also true for other hypergraph invariants such as treewidth, cutset-width, and so on. However, the advantage of hypertree width with respect to other known hypergraph invariants is that it is more general and covers larger classes of instances of bounded width. The main concepts of hypertree decomposition and hypertree width are introduced in the present section. A normal form for hypertree decompositions is described in Section 2. Section 3 describes the Robbers and Marshals game which caracterizes hypertree-width. In Section 4 we use this game to explain why the problem of checking whether the hypertree width of a hypergraph is ≤ k is feasible in polynopmial time for each constant k. However, in Section 5 we show that this problem is fixed-parameter intractable with respect to k. In Section 6 we compare hypertree width to other relevant hypergraph invariants. In Section 7 we discuss heuristics for computing hypertree decompositions. In Section 8 we show how hypertree decompositions can be beneficially applied for solving constraint satisfaction problems (CSPs). Finally, in Section 9 we list some open problems left for future research. Due to space limitations this paper is rather short, and most proofs are missing. A more thorough treatment of can be found in [14,17,2,1,16,18], most of which are available at the Hypertree Decompositions Homepage at http://si.deis.unical.it/ frank/Hypertrees. A hypergraph is a pair H = (V (H), E(H)), consisting of a nonempty set V (H) of vertices, and a set E(H) of subsets of V (H), the hyperedges of H. We only consider finite hypergraphs. Graphs are hypergraphs in which all hyperedges have two elements.For a hypergraph H and a set X ⊆ V (H), the subhypergraph induced by X is the hypergraphThe primal graph of a hypergraph H is the graph H = (V (H), {{v, w} | v = w, there exists an e ∈ E(H) such that {v, w} ⊆ e}).is connected, and a connected component of H is a maximal connected subset of V (H). A sequence of nodes of V (H) is a path of H if it is a path of H.A tree decomposition of a hypergraph H is a tuple (T, χ), where T = (V (T ), E(T )) is a tree and χ : V (T ) −→ 2 V (H) is a function associating a set of vertices χ(t) ⊆ V (H) to each vertex t of the
The Travelling Salesman Problem (TSP) has been extensively studied in the literature and various solvers are available. However, none of the state-of-the-art solvers for TSP outperforms the others in all problem instances within a given time limit. Therefore, the prediction of the best performing algorithm can save computational resources and optimise the results. In this paper, the TSP is studied in context of automated algorithm selection. Our aim is to identify the relevant features of problem instances and tackle this scenario as a machine learning task. We extend the set of existing features in the literature and propose several novel features to better characterise the problem. The contribution of the new features is statistically analysed and experiments show that adding our new features improves the prediction accuracy. We identified that our features based on kNN graph transformation are especially helpful.To create the training datasets, two state-of-the-art (meta-)heuristic algorithms are systematically evaluated on more than 2000 problems. Overall, we show that our prediction can be substantially more accurate than simple preference of an algorithm with the best performance for a majority of problem instances.
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