219 research outputs found
Tractable Optimization Problems through Hypergraph-Based Structural Restrictions
Several variants of the Constraint Satisfaction Problem have been proposed
and investigated in the literature for modelling those scenarios where
solutions are associated with some given costs. Within these frameworks
computing an optimal solution is an NP-hard problem in general; yet, when
restricted over classes of instances whose constraint interactions can be
modelled via (nearly-)acyclic graphs, this problem is known to be solvable in
polynomial time. In this paper, larger classes of tractable instances are
singled out, by discussing solution approaches based on exploiting hypergraph
acyclicity and, more generally, structural decomposition methods, such as
(hyper)tree decompositions
Computing hypergraph width measures exactly
Hypergraph width measures are a class of hypergraph invariants important in
studying the complexity of constraint satisfaction problems (CSPs). We present
a general exact exponential algorithm for a large variety of these measures. A
connection between these and tree decompositions is established. This enables
us to almost seamlessly adapt the combinatorial and algorithmic results known
for tree decompositions of graphs to the case of hypergraphs and obtain fast
exact algorithms.
As a consequence, we provide algorithms which, given a hypergraph H on n
vertices and m hyperedges, compute the generalized hypertree-width of H in time
O*(2^n) and compute the fractional hypertree-width of H in time
O(m*1.734601^n).Comment: 12 pages, 1 figur
A Backtracking-Based Algorithm for Computing Hypertree-Decompositions
Hypertree decompositions of hypergraphs are a generalization of tree
decompositions of graphs. The corresponding hypertree-width is a measure for
the cyclicity and therefore tractability of the encoded computation problem.
Many NP-hard decision and computation problems are known to be tractable on
instances whose structure corresponds to hypergraphs of bounded
hypertree-width. Intuitively, the smaller the hypertree-width, the faster the
computation problem can be solved. In this paper, we present the new
backtracking-based algorithm det-k-decomp for computing hypertree
decompositions of small width. Our benchmark evaluations have shown that
det-k-decomp significantly outperforms opt-k-decomp, the only exact hypertree
decomposition algorithm so far. Even compared to the best heuristic algorithm,
we obtained competitive results as long as the hypergraphs are not too large.Comment: 19 pages, 6 figures, 3 table
Structural Decompositions for Problems with Global Constraints
A wide range of problems can be modelled as constraint satisfaction problems
(CSPs), that is, a set of constraints that must be satisfied simultaneously.
Constraints can either be represented extensionally, by explicitly listing
allowed combinations of values, or implicitly, by special-purpose algorithms
provided by a solver.
Such implicitly represented constraints, known as global constraints, are
widely used; indeed, they are one of the key reasons for the success of
constraint programming in solving real-world problems. In recent years, a
variety of restrictions on the structure of CSP instances have been shown to
yield tractable classes of CSPs. However, most such restrictions fail to
guarantee tractability for CSPs with global constraints. We therefore study the
applicability of structural restrictions to instances with such constraints.
We show that when the number of solutions to a CSP instance is bounded in key
parts of the problem, structural restrictions can be used to derive new
tractable classes. Furthermore, we show that this result extends to
combinations of instances drawn from known tractable classes, as well as to CSP
instances where constraints assign costs to satisfying assignments.Comment: The final publication is available at Springer via
http://dx.doi.org/10.1007/s10601-015-9181-
Approximating acyclicity parameters of sparse hypergraphs
The notions of hypertree width and generalized hypertree width were
introduced by Gottlob, Leone, and Scarcello in order to extend the concept of
hypergraph acyclicity. These notions were further generalized by Grohe and
Marx, who introduced the fractional hypertree width of a hypergraph. All these
width parameters on hypergraphs are useful for extending tractability of many
problems in database theory and artificial intelligence. In this paper, we
study the approximability of (generalized, fractional) hyper treewidth of
sparse hypergraphs where the criterion of sparsity reflects the sparsity of
their incidence graphs. Our first step is to prove that the (generalized,
fractional) hypertree width of a hypergraph H is constant-factor sandwiched by
the treewidth of its incidence graph, when the incidence graph belongs to some
apex-minor-free graph class. This determines the combinatorial borderline above
which the notion of (generalized, fractional) hypertree width becomes
essentially more general than treewidth, justifying that way its functionality
as a hypergraph acyclicity measure. While for more general sparse families of
hypergraphs treewidth of incidence graphs and all hypertree width parameters
may differ arbitrarily, there are sparse families where a constant factor
approximation algorithm is possible. In particular, we give a constant factor
approximation polynomial time algorithm for (generalized, fractional) hypertree
width on hypergraphs whose incidence graphs belong to some H-minor-free graph
class
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