The dominating set problem (DSP) is one of the most famous problems in combinatorial optimization. It is defined as follows. For a given graph G=(V,E), a dominating set of G is a subset S⊆V such that every vertex in V∖S is adjacent to at least one vertex in S. Furthermore, the DSP is the problem of finding a minimum-size dominating set and the corresponding minimum size, the domination number of G. In this, work we investigate a variant of the DSP, the super dominating set problem (SDSP), which has attracted much attention during the last years. A dominating set S is called a super dominating set of G, if for every vertex u∈S¯=V∖S, there exists a v∈S such that N(v)∩S¯=N(v)∖S={u}. Analogously, the SDSP is to find a minimum-size super dominating set, and the corresponding minimum size, the super domination number of G. The decision variants of both the DSP and the SDSP have been shown to be NP-hard. In this paper, we present tight bounds for the super domination number of the neighbourhood corona product, r-clique sum, and the Hajós sum of two graphs. Additionally, we present infinite families of graphs attaining our bounds. Finally, we give the exact number of minimum size super dominating sets for some graph classes. In particular, the number of super dominating sets for cycles has quite surprising properties as it varies between values of the set {4,n,2n,5n2−10n8} based on nmod4.

We determine the sensitivity of a current optimal solution to a combinatorial optimization problem to cost changes in a set of elements. In a recent study, the concept of regular set tolerances has been introduced for a combinatorial optimization problem and for three types of cost functions, namely sum, product, and bottleneck. A regular set tolerance is the supremum amount of cost changes that can be distributed in the most favorable way to multiple elements such as not to change the current optimal solution. In this paper, we introduce an alternative concept, namely the reverse set tolerance, which is a measure of the infimum amount of cost changes to multiple elements such that the current optimal solution becomes non-optimal. We characterize the specific cases in which reverse set upper and lower tolerances have positive values and in which they are infinite. We also show a criterion for the uniqueness of an optimal solution. Furthermore, we present bounds and exact formulas for reverse set upper and lower tolerances using the relation to their corresponding single tolerance counterparts. We discuss the similarities and differences in the results between regular and reverse set tolerances. Finally, we motivate this new concept by analyzing them for special combinatorial optimization problems with important practical applications.

This paper considers combinatorial optimization problems with an objective of type bottleneck, so the objective is to minimize the maximum cost among all elements in a feasible solution. For these problems, the sensitivity of an optimal solution to changes in parameters has received much less attention in existing studies than the computation of an optimal solution. This paper introduces methods for computing upper and lower tolerances which measure the amount of cost change needed in an element inside and outside an optimal solution, respectively, before that solution becomes non-optimal. Our main contribution is the development of efficient computation methods for bottleneck versions of the Linear Assignment Problem and the Minimum Spanning Tree Problem.

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.

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.

The Hamiltonian cycle problem (HCP) is an important combinatorial problem with applications in many areas. It is among the first problems used for studying intrinsic properties, including phase transitions, of combinatorial problems. While thorough theoretical and experimental analyses have been made on the HCP in undirected graphs, a limited amount of work has been done for the HCP in directed graphs (DHCP). The main contribution of this work is an effective algorithm for the DHCP. Our algorithm explores and exploits the close relationship between the DHCP and the Assignment Problem (AP) and utilizes a technique based on Boolean satisfiability (SAT). By combining effective algorithms for the AP and SAT, our algorithm significantly outperforms previous exact DHCP algorithms, including an algorithm based on the award-winning Concorde TSP algorithm. The second result of the current study is an experimental analysis of phase transitions of the DHCP, verifying and refining a known phase transition of the DHCP.

MOTIVATION: Inference of haplotypes from genotype data is crucial and challenging for many vitally important studies. The first, and most critical step, is the ascertainment of a biologically sound model to be optimized. Many models that have been proposed rely partially or entirely on reducing the number of unique haplotypes in the solution. RESULTS: This article examines the parsimony of haplotypes using known haplotypes as well as genotypes from the HapMap project. Our study reveals that there are relatively few unique haplotypes, but not always the least possible, for the datasets with known solutions. Furthermore, we show that there are frequently very large numbers of parsimonious solutions, and the number increases exponentially with increasing cardinality. Moreover, these solutions are quite varied, most of which are not consistent with the true solutions. These results quantify the limitations of the Pure Parsimony model and demonstrate the imperative need to consider additional properties for haplotype inference models. At a higher level, and with broad applicability, this article illustrates the power of combinatorial methods to tease out imperfections in a given biological model.

Abstract We consider the problem of connecting a set 1 of n inputs to a set O of N outputs (n ≤ N) by as few edges as possible such that for every injective mapping f : I → O there are n vertex disjoint paths from i to f(i) of length k for a given k ∈ IN. For k = Ω(logN + log2n) Oruç (J Parallet Distributed Comput 1994, 359–366 10 ) gave the presently best (n,N)‐connector with O(N + n · logn) edges. For k = 2 and N the square of a prime, Richards and Hwang (1985) described a construction using $N\lceil\sqrt{n + 5/4} - 1/2\rceil + n\lceil\sqrt{n + 5/4} - 1/2 \rceil \sqrt{N}$ edges. We show by a probabilistic argument that an optimal (n,N)‐connector has Θ(N) edges, if n ≤ N½−ε for some ∈ ≥ 0. Moreover, we give explicit constructions based on a new number theoretic approach that need at most $N\lceil \sqrt{3n/4}\rceil + 2n\lceil \sqrt {3n/4}\rceil\lceil\sqrt{N}\rceil$ edges for arbitrary choices of n and N. The improvement we achieve is based on applying a generalization of the Erdős‐Heilbronn conjecture on the size of restricted sums. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 45(3), 119–124 2005

Summary form only given. Smith normal form computation is important in many topics, e.g. group theory and number theory. For matrices over the rings Z and F/sub 2/[x], we introduce a new Smith normal form algorithm, called triangular band matrix algorithm, which first computes the Hermite normal form and then step by step the diagonal form and the Smith normal form. In comparison to the Kannan Bachem algorithm, which computes the Smith normal form by alternately computing the Hermite normal form and the left Hermite normal form, the theoretical advantage is, that we only once apply the expensive Hermite normal form step. We parallelize the triangular band matrix algorithm and get a better complexity analysis than for previous parallel algorithms, like the Kannan Bachem algorithm and the Hartley Hawkes algorithm. In the part, which is different to the Kannan Bachem algorithm, the triangular band matrix algorithm leads to a better efficiency and smaller execution times, even for large example matrices.