Integer k-matching preclusion of graphs

As a generalization of matching preclusion number of a graph, we provide the (strong) integer $k$-matching preclusion number, abbreviated as $MP^{k}$ number ($SMP^{k}$ number), which is the minimum number of edges (vertices and edges) whose deletion results in a graph that has neither perfect integer $k$-matching nor almost perfect integer $k$-matching. In this paper, we show that when $k$ is even, the ($SMP^{k}$) $MP^{k}$ number is equal to the (strong) fractional matching preclusion number. We obtain a necessary condition of graphs with an almost-perfect integer $k$-matching and a relational expression between the matching number and the integer $k$-matching number of bipartite graphs. Thus the $MP^{k}$ number and the $SMP^{k}$ number of complete graphs, bipartite graphs and arrangement graphs are obtained, respectively.Comment: 18 pages, 5 figure

Fractional strong matching preclusion for two variants of hypercubes

Let F be a subset of edges and vertices of a graph G. If G-F has no fractional perfect matching, then F is a fractional strong matching preclusion set of G. The fractional strong matching preclusion number is the cardinality of a minimum fractional strong matching preclusion set. In this paper, we mainly study the fractional strong matching preclusion problem for two variants of hypercubes, the multiply twisted cube and the locally twisted cube, which are two of the most popular interconnection networks. In addition, we classify all the optimal fractional strong matching preclusion set of each

Robust Assignments via Ear Decompositions and Randomized Rounding

Many real-life planning problems require making a priori decisions before all parameters of the problem have been revealed. An important special case of such problem arises in scheduling problems, where a set of tasks needs to be assigned to the available set of machines or personnel (resources), in a way that all tasks have assigned resources, and no two tasks share the same resource. In its nominal form, the resulting computational problem becomes the \emph{assignment problem} on general bipartite graphs. This paper deals with a robust variant of the assignment problem modeling situations where certain edges in the corresponding graph are \emph{vulnerable} and may become unavailable after a solution has been chosen. The goal is to choose a minimum-cost collection of edges such that if any vulnerable edge becomes unavailable, the remaining part of the solution contains an assignment of all tasks. We present approximation results and hardness proofs for this type of problems, and establish several connections to well-known concepts from matching theory, robust optimization and LP-based techniques.Comment: Full version of ICALP 2016 pape

Fractional matching preclusion for butterfly derived networks

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu [18] recently introduced the concept of fractional matching preclusion number. The fractional matching preclusion number (FMP number) of G, denoted by fmp(G), is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The fractional strong matching preclusion number (FSMP number) of G, denoted by fsmp(G), is the minimum number of vertices and edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we study the fractional matching preclusion number and the fractional strong matching preclusion number for butterfly network, augmented butterfly network and enhanced butterfly network

Bulk-robust assignment problems: hardness, approximability and algorithms

This thesis studies robust assignment problems with focus on computational complexity. Assignment problems are well-studied combinatorial optimization problems with numerous practical applications, for instance in production planning. Classical approaches to optimization expect the input data for a problem to be given precisely. In contrast, real-life optimization problems are modeled using forecasts resulting in uncertain problem parameters. This fact can be taken into account using the framework of robust optimization. An instance of the classical assignment problem is represented using a bipartite graph accompanied by a cost function. The goal is to find a minimum-cost assignment, i.e., a set of resources (edges or nodes in the graph) defining a maximum matching. Most models for robust assignment problems suggested in the literature capture only uncertainty in the costs, i.e., the task is to find an assignment minimizing the cost in a worst-case scenario. The contribution of this thesis is the introduction and investigation of the Robust Assignment Problem (RAP) which models edge and node failures while the costs are deterministic. A scenario is defined by a set of resources that may fail simultaneously. If a scenario emerges, the corresponding resources are deleted from the graph. RAP seeks to find a set of resources of minimal cost which is robust against all possible incidents, i.e., a set of resources containing an assignment for all scenarios. In production planning for example, lack of materials needed to complete an order can be encoded as an edge failure and production line maintenance corresponds to a node failure. The main findings of this thesis are hardness of approximation and NP-hardness results for both versions of RAP, even in case of single edge (or node) failures. These results are complemented by approximation algorithms matching the theoretical lower bounds asymptotically. Additionally, we study a new related problem concerning k-robust matchings. A perfect matching in a graph is $k$-robust if the graph remains perfectly matchable after the deletion of any k matching edges from the graph. We address the following question: How many edges have to be added to a graph to make a fixed perfect matching k-robust? We show that, in general, this problem is as hard as both aforementioned variants of RAP. From an application point of view, this result implies that robustification of an existent infrastructure is not easier than designing a new one from scratch.Diese Dissertation behandelt robuste Zuordnungsprobleme mit dem Schwerpunkt auf deren komlexitätstheoretischen Eigenschaften. Zuordnungsprobleme sind gut untersuchte kombinatorische Optimierungsprobleme mit vielen praktischen Anwendungen, z. B. in der Produktionsplanung. Klassische Ansätze der Optimierung gehen davon aus, dass die Inputdaten eines Problems exakt gegeben sind, wohingegen Optimierungsprobleme aus der Praxis mit Hilfe von Voraussagen modelliert werden. Daraus folgen unsichere Problemparameter, woran die Robuste Optimierung ansetzt. Die Unsicherheit wird mit Hilfe einer Szenarienmenge modelliert, die alle möglichen Ausprägungen der Problemparameter beschreibt. Eine Instanz des klassischen Zordnungsproblems wird mit Hilfe eines Graphen und einer Kostenfunktion beschrieben. Die Aufgabe besteht darin, eine Zuordnung mit minimalen Kosten zu finden. Eine Zuordnung ist eine Teilmenge an Ressourcen (Kanten oder Knoten des Graphen), die ein kardinalitätsmaximales Matching induziert. In der Literatur sind überwiegend robuste Zuordnungsprobleme untersucht, die Unsicherheit in den Kosten behandeln, in diesem Fall besteht die Aufgabe darin, eine Zuordnung mit minimalen Kosten im Worst-Case-Szenario zu finden. Diese Dissertation dient der Einführung und Untersuchung des Robust Assignment Problem (RAP) welches Kanten- und Knotenausfälle modelliert; wobei die Kosten determinisitsch sind. Ein Szenario ist durch jene Teilmenge an Ressourcen definiert, welche gleichzeitig ausfallen können. Wenn ein Szenario eintritt, werden die jeweils ausfallenden Ressourcen aus dem Graphen entfernt. In RAP besteht das Ziel darin, eine Menge an Ressourcen mit minimalen Kosten zu finden, die robust gegenüber allen möglichen Ereignissen ist, d. h. eine Ressourcenmenge die für alle Szenarien eine gültige Zuordnung enthält. So kann beispielsweise in der Produktionsplanung der Mangel an Materialien, die für einen Auftrag benötigt werden, als Kantenausfall und die wartungsbedingte Abschaltung einer Produktionslinie als Knotenausfall modelliert werden. Die Hauptergebnisse dieser Arbeit sind Nichtapproximierbarkeits- und NP-Schwierigkeitsresultate beider RAP-Versionen, die bereits für die Einschränkung zutreffen, dass nur einzelne Kanten oder Knoten ausfallen können. Diese Ergebnisse werden durch Approximationsalgorithmen ergänzt, die die theoretischen Approximationsschranken asymptotisch erreichen. Zusätzlich wird ein neues, verwandtes Optimierungsproblem untersucht, welches sich mit k-robusten Matchings beschäftigt. Ein perfektes Matching in einem Graphen ist k-robust, wenn der Graph nach dem Löschen von k Matchingkanten weiterhin ein perfektes Matching besitzt. Es wird der Frage nachgegangen, wie viele Kanten zum Graphen hinzugefügt werden müssen, um ein gegebenes Matching k-robust zu machen. Dabei wird gezeigt, dass dieses Problem im Allgemeinen aus komplexitätstheoretischer Sicht genauso schwierig ist, wie die zuvor erwähnten RAP-Varianten. Aus der Anwendungsperspektive bedeutet dieses Resultat, dass die Robustifikation einer bestehender Infrastruktur nicht einfacher ist, als sie von Grund auf neu zu entwerfen

The characterisation of growth hormone-related cardiac disease with magnetic resonance imaging & The effects of growth hormone dysregulation on adenosine monophosphate-activated protein kinase in cardiac tissue

PhDChronic growth hormone (GH) excess, acromegaly, causes a specific cardiomyopathy, which remains poorly understood. The pattern of hypertrophy is distinct from other forms of cardiac disease and begins to appear before hypertension or diabetes. GH deficiency (GHD) also causes cardiovascular problems, with reduced ability to mount a cardiovascular response to exercise. Acromegaly and GHD patients have increased cardiac mortality. Cardiac magnetic resonance imaging (CMR) is the gold standard for assessment of cardiac mass and provides data on cardiac function, fibrosis, valve function and ischaemia. This study used CMR to assess 23 patients with acromegaly or GHD, before and after treatment of their GH disorder, and 23 healthy controls. Patients with acromegaly demonstrated increased left ventricular mass index (LVMi), end diastolic volume index, stroke volume index and cardiac index, which persisted at one year, despite treatment of underlying disease. Patients with GHD demonstrated LVMi at the bottom (males) or beneath (females) published normal references ranges, which increased with one year of GH replacement. The mechanisms by which GH influences cardiac tissue are poorly understood. Adenosine monophosphate-activated protein kinase (AMPK) is an energy-regulator enzyme, which interacts with several metabolic hormones. Mutations in AMPK cause arrhythmias and cardiac hypertrophy. AMPK activation may be a mechanism by which GH causes some of its cardiac effects. This study used primary cardiomyocytes and mouse and rat models of GH excess and deficiency to study the effects of GH on cardiac AMPK. Acute GH treatment increased AMPK activity in both in vivo and in vitro studies; acute IGF-I treatment had the opposite effect. In 2 and 8 month old bovine GH-overexpressing (bGH) and GH receptor knock out (GHRKO) mice, functional AMPK assay did not demonstrate any difference in cardiac AMPK activity between transgenics and controls. However, Western blotting for Threonine-172 phospho (p)AMPK levels, a marker of AMPK activity, demonstrated increased cardiac pAMPK in 2 month old bGH mice and a reduction in cardiac pAMPK levels in 8 month old animals. A trend towards the same findings was seen in GHRKO mice. This indicates that both GH and IGF-I interact with myocardial AMPK, apparently via different mechanisms