Search

Many problems on graphs can be expressed in the following language: given a graph G = ( V , E ) and a terminal set T ⊆ V , find a minimum size set S ⊆ V which intersects all “structures” (such as cycles or paths) passing through the vertices in T . We refer to this class of problems as terminal set problems. In this paper, we introduce a general method to obtain faster exact exponential time algorithms for several terminal set problems. In the process, we break the O ⁎ ( 2 n ) barrier for the classic Node Multiway Cut , Directed Unrestricted Node Multiway Cut and Directed Subset Feedback Vertex Set problems. [ABSTRACT FROM AUTHOR]

Abstract: Minimum Fill-in is a fundamental and classical problem arising in sparse matrix computations. In terms of graphs it can be formulated as a problem of finding a triangulation of a given graph with the minimum number of edges. In this paper, we study the parameterized complexity of local search for the Minimum Fill-in problem in the following form: Given a triangulation H of a graph G, is there a better triangulation, i.e. triangulation with less edges than H, within a given distance from H? We prove that this problem is fixed-parameter tractable (FPT) being parameterized by the distance from the initial triangulation, by providing an algorithm that in time decides if a better triangulation of G can be obtained by swapping at most k edges of H. Our result adds Minimum Fill-in to the list of very few problems for which local search is known to be FPT. [Copyright &y& Elsevier]

Abstract: With the advent of new sequencing technologies able to produce an enormous quantity of short genomic sequences, new tools able to search for them inside a genomic reference sequence have emerged. Because of chemical reading errors or of the variability between organisms, one is interested in finding not only exact occurrences, but also occurrences with up to k mismatches. The contribution of this paper is twofold. On the one hand, we present a generalization of the classical Rabin–Karp string matching algorithm to solve the k-mismatch problem, with average complexity (n text and m pattern lengths, respectively). On the other hand, we show how to employ this idea in conjunction with an index over the text, allowing to search a pattern, with up to k mismatches, in time proportional to its length. This novel tool—rNA (randomized Numerical Aligner)—is in general faster and more accurate than other available tools like SOAP2, BWA, and BOWTIE. rNA executables and source code are freely available at http://iga-rna.sourceforge.net/ . [Copyright &y& Elsevier]

Abstract: We consider the XPath evaluation problem: Evaluate an XPath query Q on a streaming XML document D. We consider two versions of the problem: 1) Filtering Problem: Determine if there is a match for Q in D. 2) Node Selection Problem: Determine the set of document nodes selected by Q. We consider Conjunctive XPath (CXPath) queries that involve only the child and descendant axes. Let d denote the depth of D, and n denote the number of location steps in Q. Bar-Yossef et al. (2007, 2005) presented lower bounds on the memory space required by any algorithm to solve these two problems. Their lower bounds apply to each query in a large subset of XPath, and are obtained (mostly) using nonrecursive . In this paper, we present larger lower bounds for a different class of queries (namely, CXPath queries with independent predicates), on recursive . One of our results is an lower bound for the node selection problem, for a worst-case Q; is the maximum number of nodes of D that can be candidates for output, at any one instant. So, there is no algorithm for the node selection problem that uses space, for any function f. This shows that some previously published algorithms are incorrect. [Copyright &y& Elsevier]

Abstract: Kernelization is a strong and widely-applied technique in parameterized complexity. A kernelization algorithm, or simply a kernel, is a polynomial-time transformation that transforms any given parameterized instance to an equivalent instance of the same problem, with size and parameter bounded by a function of the parameter in the input. A kernel is polynomial if the size and parameter of the output are polynomially-bounded by the parameter of the input. In this paper we develop a framework which allows showing that a wide range of FPT problems do not have polynomial kernels. Our evidence relies on hypothesis made in the classical world (i.e. non-parametric complexity), and revolves around a new type of algorithm for classical decision problems, called a distillation algorithm, which is of independent interest. Using the notion of distillation algorithms, we develop a generic lower-bound engine that allows us to show that a variety of FPT problems, fulfilling certain criteria, cannot have polynomial kernels unless the polynomial hierarchy collapses. These problems include k-Path, k-Cycle, k-Exact Cycle, k-Short Cheap Tour, k-Graph Minor Order Test, k-Cutwidth, k-Search Number, k-Pathwidth, k-Treewidth, k-Branchwidth, and several optimization problems parameterized by treewidth and other structural parameters. [Copyright &y& Elsevier]

Abstract: We consider the XPath evaluation problem: Evaluate an XPath query Q on a streaming XML document D; i.e., determine the set of document elements selected by Q. We mainly consider Conjunctive XPath queries that involve only the child and descendant axes. Previously known in-memory algorithms for this problem use space and time. Several previously known algorithms for the streaming version use space and time in the worst case; d denotes the depth of D, and n denotes the number of location steps in Q. Their exponential space requirement could well exceed the space used by the in-memory algorithms. We present an efficient algorithm that uses space and time in the worst case; c denotes the maximum number of elements of D that can be candidates for output, at any one instant. For some worst case Q and D, the memory space used by our algorithm matches our lower bound proved in a different paper; so, our algorithm uses optimal memory space in the worst case. [Copyright &y& Elsevier]

Abstract: In this paper, we study the inverse sorting problem with bound constraints under the -norm and the Hamming distance. For the problem under the -norm, an -time algorithm is presented. For the problem under the Hamming distance, we first show that it has an -time lower bound in the comparison model; and then, we present an -time algorithm. Both of the presented algorithms improve the previous upper bounds from . [Copyright &y& Elsevier]

Fixed-parameter tractability (FPT) techniques have recently been successful in solving NP-complete problem instances of practical importance which were too large to be solved with previous methods. In this paper, we show how to enhance this approach through the addition of parallelism, thereby allowing even larger problem instances to be solved in practice. More precisely, we demonstrate the potential of parallelism when applied to the bounded-tree search phase of FPT algorithms. We apply our methodology to the k-Vertex Cover problem which has important applications in, for example, the analysis of multiple sequence alignments for computational biochemistry. We have implemented our parallel FPT method for the k-Vertex Cover problem using C and the MPI communication library, and tested it on a 32-node Beowulf cluster. This is the first experimental examination of parallel FPT techniques. As part of our experiments, we solved larger instances of k-Vertex Cover than in any previously reported implementations. For example, our code can solve problem instances with k⩾400 in less than 1.5 h. [Copyright &y& Elsevier]