Supply chain network design (SCND) identifies the production and distribution resources essential to maximizing a network’s profit. Once implemented, a SCND impacts a network’s performance for the long-term. This dissertation extends the SCND literature both in terms of model scope and solution approach. The SCND problem can be more realistically modeled to improve design decisions by including: the location, capacity, and technology attributes of a resource; the effect of the economies of scale on the cost structure; multiple products and multiple levels of supply chain hierarchy; stochastic, dynamic, and correlated demand; and the gradually unfolding uncertainty. The resulting multistage stochastic mixed-integer program (MSMIP) has no known general purpose solution methodology. Two decomposition approaches—end-of-horizon (EoH) decomposition and nodal decomposition—are applied. The developed EoH decomposition exploits the traditional treatment of the end-of-horizon effect. It rests on independently optimizing the SCND of every node of the last level of the scenario-tree. Imposing these optimal configurations before optimizing the design decisions of the remaining nodes produces a smaller and thus easier to solve MSMIP. An optimal solution results when the discount rate is 0 percent. Otherwise, this decomposition deduces a bound on the optimality-gap. This decomposition is neither SCND nor MSMIP specific; it pertains to any application sensitive to the EoH-effect and to special cases of MSMIP. To demonstrate this versatility, additional computational experiments for a two-stage mixed-integer stochastic program (SMIP) are included. This dissertation also presents the first application of nodal decomposition in both SCND and MSMIP. The developed column generation heuristic optimizes the nodal sub-problems using an iterative procedure that provides a restricted master problem’s columns. The heuristic’s computational efficiency rests on solving the sub-problems independently and on its novel handling of the master problem. Conceptually, it reformulates the master problem to avoid the duality-gap. Technologically, it provides the first application of Leontief substitution flow problems in MSMIP and thereby shows that hypergraphs lend themselves to loosely coupled MSMIPs. Computational results demonstrate superior performance of the heuristic approach and also show how this heuristic still applies when the SCND problem is modeled as a SMIP where the restricted master problem is a shortest-path problem.

Classical partitioning problems, like the Max-Cut problem, ask for partitions that optimize one quantity, which are important to such fields as VLSI design, combinatorial optimization, and computer science. Judicious partitioning problems on graphs or hypergraphs ask for partitions that optimize several quantities simultaneously. In this dissertation, we work on judicious partitions of graphs and hypergraphs, and solve or asymptotically solve several open problems of Bollobas and Scott on judicious partitions, using the probabilistic method and extremal techniques.

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No description available.

For a hypergraph H=(V,E) and a field F, a weighting of H is a map f:V ?F. A weighting is called stable if there is some k ? F such that the sum of the weights on each edge of H is equal to k. The set of all stable weightings of H forms a vector space over F. This vector space is termed the uniformity space of H over F, denoted U(H,F), and its dimension is called the uniformity dimension of H over F. This thesis is concerned with several problems relating to the uniformity space of hypergraphs. For several families of hypergraphs, simple ways of computing their uniformity dimension are found. Also, the uniformity dimension of random l-uniform hypergraphs is investigated. The stable weightings of the spanning trees of a graph are determined, and lastly, a notion of critical uniformity dimension is introduced and explored.

A k-uniform hypergraph N = (X. B) of order n is a family of k-subsets B of an n-set X. A k-uniform hypergraph 7--L = (X. B) is disjoint union-free (DUF) if all disjoint pairs of elements of B have distinct unions; that is, if for every A, B, C, D E B. A fl B = C f1 D = 0 and A U B =CUD implies {A. B} = {C, D}. DUF families of maximum size have been studied by Erdos and Fiiredi. and in the case k = 3 this maximum size has been conjectured to equal (z). In this thesis, we study DUF 3-uniform hypergraphs with the main goals of presenting evidence to support this conjecture and studying the structures that have conjectured maximum size. If each pair of distinct elements of X is covered exactly A times in B then we call N = (X, B) an (n. k. A)-design. Using a blend of graph- and design-theoretic techniques, we study the DUF (n,. 3. 3)-designs that are the conjectured unique structures having maximum size. Central results of this thesis include substantially improving lower bounds on the maximum size for a large class of n. giving conditions on pair coverage in a DUF 3-uniform hypergraph that force an (n., 3, 3)-design, and providing constructions for DUF 3-uniform hypergraphs from families of DUF hypergraphs with smaller orders. Let. N = (X, B) be a DUF k-uniform hypergraph with the property that 7-t U {E} is not DUF for any k-subset E of X not already in H. Then N is maximally DUF. We introduce the problem of finding the minimum size of maximally DUF families and provide bounds on this quantity for k = 3.

Thesis (M.S.)--Ohio University, November, 2006. / Title from PDF t.p. Includes bibliographical references.

Hypergraph partitioning lies at the heart of a number of problems in machine learning as well as other engineering disciplines. While partitioning uniform hypergraphs is often required in computer vision problems that involve multi-way similarities, non-uniform hypergraph partitioning has applications in database systems, circuit design etc. As in the case of graphs, it is known that for given objective and balance constraints, the problem of optimally partitioning a hypergraph is NP-hard. Yet, over the last two decades, several efficient heuristics have been studied in the literature and their empirical success is widely appreciated. In contrast to the extensive studies related to graph partitioning, the theoretical guarantees of hypergraph partitioning approaches have not received much attention in the literature. The purpose of this thesis is to establish the statistical error bounds for certain spectral algorithms for partitioning uniform as well as non-uniform hypergraphs. The mathematical framework considered in this thesis is the following. Let V be a set of n vertices, and ψ : V ->{1,…,k} be a (hidden) partition of V into k classes. A random hypergraph (V,E) is generated according to a planted partition model, i.e., subsets of V are independently added to the edge set E with probabilities depending on the class memberships of the participating vertices. Let ψ' be the partition of V obtained from a certain algorithm acting on a random realization of the hypergraph. We provide an upper bound on the number of disagreements between ψ and ψ'. To be precise, we show that under certain conditions, the asymptotic error is o(n) with probability (1-o(1)). In the existing literature, such error rates are only known in the case of graphs (Rohe et al., Ann. Statist., 2011; Lei \& Rinaldo, Ann. Statist., 2015), where the planted model coincides with the popular stochastic block model. Our results are based on matrix concentration inequalities and perturbation bounds, and the derived bounds can be used to comment on the consistency of spectral hypergraph partitioning algorithms. It is quite common in the literature to resort to a spectral approach when the quantity of interest is a matrix, for instance, the adjacency or Laplacian matrix for graph partitioning. This is certainly not true for hypergraph partitioning as the adjacency relations cannot be encoded into a symmetric matrix as in the case of graphs. However, if one restricts the problem to m-uniform hypergraphs for some m ≥ 2, then a symmetric tensor of order m can be used to express the multi-way interactions or adjacencies. Thus, the use of tensor spectral algorithms, based on the spectral theory of symmetric tensors, is a natural choice in this scenario. We observe that a wide variety of uniform hypergraph partitioning methods studied in the literature can be related to any one of two principle approaches: (1) solving a tensor trace maximization problem, or (2) use of the higher order singular value decomposition of tensors. We derive statistical error bounds to show that both these approaches lead to consistent partitioning algorithms. Our results also hold when the hypergraph under consideration allows weighted edges, a situation that is commonly encountered in computer vision applications such as motion segmentation, image registration etc. In spite of the theoretical guarantees, a tensor spectral approach is not preferable in this setting due to the time and space complexity of computing the weighted adjacency tensor. Keeping this practical scenario in mind, we prove that consistency can still be achieved by incorporating certain tensor sampling strategies. In particular, we show that if the edges are sampled according to certain distribution, then consistent partitioning can be achieved with only few sampled edges. Experiments on benchmark problems demonstrate that such sampled tensor spectral algorithms are indeed useful in practice. While vision tasks mostly involve uniform hypergraphs, in database and electronics applications, one often finds non-uniform hypergraphs with edges of varying sizes. These hypergraphs cannot be expressed in terms of adjacency matrices or tensors, and hence, use of a spectral approach is tricky in this context. The partitioning problem gets more challenging due to the fact that, in practice, these hypergraphs are quite sparse, and hence, provide less information about the partition. We consider spectral algorithms for partitioning clique and star expansions of hypergraphs, and study their consistency under a sparse planted partition model. The results of hypergraph partitioning can be further extended to address the well-known hypergraph vertex coloring problem, where the objective is to color the vertices such that no edge is monochromatic. The hardness of this problem is well established. In fact, even when a hypergraph is bipartite or 2-colorable, it is NP-hard to find a proper 2-coloring for it. We propose a spectral coloring algorithm, and show that if the non-monochromatic subsets of vertices are independently added to the edge set with certain probabilities, then with probability (1-o(1)), our algorithm succeeds in coloring bipartite hypergraphs with only two colors. To the best our knowledge, these are the first known results related to consistency of partitioning general hypergraphs.

Dois problemas combinatórios são estudados: (i) determinar a quantidade de cópias de um hipergrafo fixo em um hipergrafo uniforme pseudoaleatório, e (ii) estimar números de Ramsey de ordem dois e três para grafos com largura de banda pequena e grau máximo limitado. Apresentamos um lema de contagem para estimar a quantidade de cópias de um hipergrafo k-uniforme linear livre de conectores (conector é uma generalização de triângulo, para hipergrafos) que estão presentes em um hipergrafo esparso pseudoaleatório G. Considere um hipergrafo k-uniforme linear H que é livre de conectores e um hipergrafo k-uniforme G com n vértices. Seja d_H=\\max\\{\\delta(J): J\\subset H\\} e D_H=\\min\\{k d_H,\\Delta(H)\\}. Estabelecemos que, se os vértices de G não possuem grau grande, famílias pequenas de conjuntos de k-1 elementos de V(G) não possuem vizinhança comum grande, e a maioria dos pares de conjuntos em {V(G)\\choose k-1} possuem a quantidade ``correta\'\' de vizinhos, então a quantidade de imersões de H em G é (1+o(1))n^{|V(H)|}p^{|E(H)|}, desde que p\\gg n^{1/D_H} e |E(G)|={n\\choose k}p. Isso generaliza um resultado de Kohayakawa, Rödl e Sissokho [Embedding graphs with bounded degree in sparse pseudo\\-random graphs, Israel J. Math. 139 (2004), 93--137], que provaram que, para p dado como acima, esse lema de imersão vale para grafos, onde H é um grafo livre de triângulos. Determinamos assintoticamente os números de Ramsey de ordem dois e três para grafos bipartidos com largura de banda pequena e grau máximo limitado. Mais especificamente, determinamos assintoticamente o número de Ramsey de ordem dois para grafos bipartidos com largura de banda pequena e grau máximo limitado, e o número de Ramsey de ordem três para tais grafos, com a suposição adicional de que ambas as classes do grafo bipartido têm aproximadamente o mesmo tamanho. / Two combinatorial problems are studied: (i) determining the number of copies of a fixed hipergraph in uniform pseudorandom hypergraphs, and (ii) estimating the two and three color Ramsey numbers for graphs with small bandwidth and bounded maximum degree. We give a counting lemma for the number of copies of linear k-uniform \\emph hypergraphs (connector is a generalization of triangle for hypergraphs) that are contained in some sparse hypergraphs G. Let H be a linear k-uniform connector-free hypergraph and let G be a k-uniform hypergraph with n vertices. Set d_H=\\max\\{\\delta(J)\\colon J\\subset H\\} and D_H=\\min\\{kd_H,\\Delta(H)\\}. We proved that if the vertices of G do not have large degree, small families of (k-1)-element sets of V(G) do not have large common neighbourhood and most of the pairs of sets in {V(G)\\choose k-1} have the `right\' number of common neighbours, then the number of embeddings of H in G is (1+o(1))n^p^, given that p\\gg n^ and |E(G)|=p. This generalizes a result by Kohayakawa, R\\\"odl and Sissokho [Embedding graphs with bounded degree in sparse pseudo\\-random graphs, Israel J. Math. 139 (2004), 93--137], who proved that, for p as above, this result holds for graphs, where H is a triangle-free graph. We determine asymptotically the two and three Ramsey numbers for bipartite graphs with small bandwidth and bounded maximum degree. More generally, we determine asymptotically the two color Ramsey number for bipartite graphs with small bandwidth and bounded maximum degree and the three color Ramsey number for such graphs with the additional assumption that both classes of the bipartite graph have almost the same size.

The total domination number γt(G) of a graph G is the minimum cardinality of a set S of vertices, so that every vertex of G is adjacent to a vertex in S. In this article, we determine an optimal upper bound on the total domination number of a graph with diameter 2. We show that for every graph G on n vertices with diameter 2, γt(G)≤1+nln(n). This bound is optimal in the sense that given any ε>0, there exist graphs G with diameter 2 of all sufficiently large even orders n such that γt(G)>(14+ε)nln(n).