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  • 1.
    Andren, Daniel
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Markström, Klas
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    The bivariate ising polynomial of a graph2009In: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 157, no 11, p. 2515-2524Article in journal (Refereed)
    Abstract [en]

    In this paper we discuss the two variable Ising polynomials in a graph theoretical setting. This polynomial has its origin in physics as the partition function of the Ising model with an external field. We prove some basic properties of the Ising polynomial and demonstrate that it encodes a large amount of combinatorial information about a graph. We also give examples which prove that certain properties, such as the chromatic number, are not determined by the Ising polynomial. Finally we prove that there exist large families of non-isomorphic planar triangulations with identical Ising polynomial. (C) 2009 Published by Elsevier B.V.

  • 2. Fischer, Anja
    et al.
    Fischer, Frank
    Jäger, Gerold
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Keilwagen, Jens
    Molitor, Paul
    Grosse, Ivo
    Exact algorithms and heuristics for the Quadratic Traveling Salesman Problem with an application in bioinformatics2014In: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 166, p. 97-114Article in journal (Refereed)
    Abstract [en]

    In this paper we introduce an extension of the Traveling Salesman Problem (TSP), which is motivated by an important application in bioinformatics. In contrast to the TSP the costs do not only depend on each pair of two nodes traversed in succession in a cycle but on each triple of nodes traversed in succession. This problem can be formulated as optimizing a quadratic objective function over the traveling salesman polytope, so we call the combinatorial optimization problem quadratic TSP (QTSP). Besides its application in bioinformatics, the QTSP is a generalization of the Angular-Metric TSP and the TSP with reload costs. Apart from the TSP with quadratic cost structure we also consider the related Cycle Cover Problem with quadratic objective function (QCCP). In this work we present three exact solution approaches and several heuristics for the QTSP. The first exact approach is based on a polynomial transformation to a TSP, which is then solved by standard software. The second one is a branch-and-bound algorithm that relies on combinatorial bounds. The best exact algorithm is a branch-and-cut approach based on an integer programming formulation with problem-specific cutting planes. All heuristical approaches are extensions of classic heuristics for the TSP. Finally, we compare all algorithms on real-world instances from bioinformatics and on randomly generated instances. In these tests, the branch-and-cut approach turned out to be superior for solving the real-world instances from bioinformatics. Instances with up to 100 nodes could be solved to optimality in about ten minutes.

  • 3.
    Jäger, Gerold
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Peczarski, Marcin
    Institute of Informatics, University of Warsaw, Poland.
    The worst case number of questions in Generalized AB game with and without white-peg answers2015In: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 184, p. 20-31Article in journal (Refereed)
    Abstract [en]

    The AB game is a two-player game, where the codemaker has to choose a secret code and the codebreaker has to guess it in as few questions as possible. It is a variant of the famous Mastermind game, with the only difference that all pegs in both, the secret and the questions must have distinct colors. In this work, we consider the Generalized AB game, where for given arbitrary numbers p, c with p <= c the secret code consists of p pegs each having one of c colors and the answer consists of a number of black and white pegs. There the number of black pegs equals the number of pegs matching in the corresponding question and the secret in position and color, and the number of white pegs equals the additional number of pegs matching in the corresponding question and the secret only in color. We consider also a variant of the Generalized AB game, where the information of white pegs is omitted. This variant is called Generalized Black-peg AB game. Let ab(p, c) and abb(p, c) be the number of questions in the worst case needed by the codebreaker to guess the secret for Generalized AB game and Generalized Black-peg AB game, respectively. Combining a computer program with theoretical considerations, we confirm known exact values of ab(2, c) and ab(3, c) and prove tight bounds for ab(4, c). Furthermore, we present exact values for abb(2, c) and abb(3, c) and tight bounds for abb(4, c)

  • 4.
    Jäger, Gerold
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Turkensteen, Marcel
    Extending Single Tolerances to Set Tolerances2018In: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 247, p. 197-215Article in journal (Refereed)
    Abstract [en]

    The theory of single upper and lower tolerances for combinatorial minimization problems was formalized in 2005 for the three types of cost functions sum, product, and maximum, and since then it has shown to be rather useful in creating heuristics and exact algorithms. However, such single tolerances are often used because the assessment of multiple cost changes is considered too complicated. This paper addresses that issue. In this paper we extend this theory from single to set tolerances for these three types of cost functions. In particular, we characterize specific values of set upper and lower tolerances as positive and infinite, and we show a criterion for the uniqueness of an optimal solution to a combinatorial minimization problem. Furthermore, we present one exact formula and several bounds for computing set upper and lower tolerances using the relation to their corresponding single tolerance counterparts. 

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