<p>Brain connectome network visualization could help the neurologists inspect the brain structure easily and quickly. In the thesis, the model of the brain connectome network is visualized in both three dimensions (3D) environment and two dimensions (2D) environment. One is named “Brain Explorer for Connectomic Analysis” (BECA) developed by the previous research already. It could present the 3D model of brain structure with region of interests (ROIs) in different colors [5]. The other is mainly for the information visualization of brain connectome in 2D. It adopts the force-directed layout to visualize the network. However, the brain network visualization could not bring the user intuitively ideas about brain structure. Sometimes, with the increasing scales of ROIs (nodes), the visualization would bring more visual clutter for readers [3]. So, brain connectome network properties visualization becomes a useful complement to brain network visualization. For a better understanding of the effect of Alzheimer’s disease on the brain nerves, the thesis introduces several methods about the brain graph properties visualization. There are the five selected graph properties discussed in the thesis. The degree and closeness are node properties. The shortest path, maximum flow, and clique are edge properties. Except for clique, the other properties are visualized in both 3D and 2D. The clique is visualized only in 2D. For the clique, a new hypergraph visualization method is proposed with three different algorithms. Instead of using an extra node to present a clique, the thesis uses a “belt” to connect all nodes within the same clique. The methods of node connections are based on the traveling salesman problem (TSP) and Law of cosines. In addition, the thesis also applies the result of the clique to adjust the force-directed layout of brain graph in 2D to dramatically eliminate the visual clutter. Therefore, with the support of the graph properties visualization, the brain connectome network visualization tools become more flexible.</p>

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Includes bibliographical references. / M.S.

Praktische Optimierungsprobleme beinhalten oft mehrere gleichberechtigte, sich jedoch widersprechende Kriterien. Beispielsweise will man bei einer Reise zugleich möglichst schnell ankommen, sie soll aber auch nicht zu teuer sein. Im ersten Teil dieser Arbeit wird die algorithmische Beherrschbarkeit solcher mehrkriterieller Optimierungsprobleme behandelt. Es werden zunächst verschiedene Lösungsbegriffe diskutiert und auf ihre Schwierigkeit hin verglichen. Interessanterweise stellt sich heraus, dass diese Begriffe für ein einkriterielles Problem stets gleich schwer sind, sie sich ab zwei Kriterien allerdings stark unterscheiden könen (außer es gilt P = NP). In diesem Zusammenhang wird auch die Beziehung zwischen Such- und Entscheidungsproblemen im Allgemeinen untersucht. Schließlich werden neue und verbesserte Approximationsalgorithmen für verschieden Varianten des Problems des Handlungsreisenden gefunden. Dabei wird mit Mitteln der Diskrepanztheorie eine Technik entwickelt, die ein grundlegendes Hindernis der Mehrkriteriellen Optimierung aus dem Weg schafft: Gegebene Lösungen so zu kombinieren, dass die neue Lösung in allen Kriterien möglichst ausgewogen ist und gleichzeitig die Struktur der Lösungen nicht zu stark zerstört wird. Der zweite Teil der Arbeit widmet sich verschiedenen Aspekten von Gleichungssystemen für (formale) Sprachen. Einerseits werden konjunktive und Boolesche Grammatiken untersucht. Diese sind Erweiterungen der kontextfreien Grammatiken um explizite Durchschnitts- und Komplementoperationen. Es wird unter anderem gezeigt, dass man bei konjunktiven Grammatiken die Vereinigungsoperation stark einschränken kann, ohne dabei die erzeugte Sprache zu ändern. Außerdem werden bestimmte Schaltkreise untersucht, deren Gatter keine Wahrheitswerte sondern Mengen von Zahlen berechnen. Für diese Schaltkreise wird das Äquivalenzproblem betrachtet, also die Frage ob zwei gegebene Schaltkreise die gleiche Menge berechnen oder nicht. Es stellt sich heraus, dass, abhängig von den erlaubten Gattertypen, die Komplexität des Äquivalenzproblems stark variiert und für verschiedene Komplexitätsklassen vollständig ist, also als (parametrisierter) Vertreter für diese Klassen stehen kann. / Practical optimization problems often comprise several incomparable and conflicting objectives. When booking a trip using several means of transport, for instance, it should be fast and at the same time not too expensive. The first part of this thesis is concerned with the algorithmic solvability of such multiobjective optimization problems. Several solution notions are discussed and compared with respect to their difficulty. Interestingly, these solution notions are always equally difficulty for a single-objective problem and they differ considerably already for two objectives (unless P = NP). In this context, the difference between search and decision problems is also investigated in general. Furthermore, new and improved approximation algorithms for several variants of the traveling salesperson problem are presented. Using tools from discrepancy theory, a general technique is developed that helps to avoid an obstacle that is often hindering in multiobjective approximation: The problem of combining two solutions such that the new solution is balanced in all objectives and also mostly retains the structure of the original solutions. The second part of this thesis is dedicated to several aspects of systems of equations for (formal) languages. Firstly, conjunctive and Boolean grammars are studied, which are extensions of context-free grammars by explicit intersection and complementation operations, respectively. Among other results, it is shown that one can considerably restrict the union operation on conjunctive grammars without changing the generated language. Secondly, certain circuits are investigated whose gates do not compute Boolean values but sets of natural numbers. For these circuits, the equivalence problem is studied, i.\,e.\ the problem of deciding whether two given circuits compute the same set or not. It is shown that, depending on the allowed types of gates, this problem is complete for several different complexity classes and can thus be seen as a parametrized) representative for all those classes.

An algorithm for solving the large-scale Traveling Salesman Problem is presented. Research into past work in the area of Hopfield neural network use in solving the Traveling Salesman Problem has yielded design ideas that have been incorporated into this work. The algorithm consists of an unsupervised learning algorithm and a recursive Hopfield neural network. The unsupervised learning algorithm was used to decompose the problem into clusters. The recursive Hopfield neural network was applied to the centroids of the clusters, then to the cities in each cluster, in order to find an optimal path. An improvement in both computation speed and solution accuracy is shown by the proposed algorithm over the straight use of the Hopfield neural network.

The first part of this thesis deals with the approximability of the traveling salesman problem. This problem is defined on a complete graph with edge weights, and the task is to find a Hamiltonian cycle of minimum weight that visits each vertex exactly once. We study the most important multiobjective variants of this problem. In the multiobjective case, the edge weights are vectors of natural numbers with one component for each objective, and since weight vectors are typically incomparable, the optimal Hamiltonian cycle does not exist. Instead we consider the Pareto set, which consists of those Hamiltonian cycles that are not dominated by some other, strictly better Hamiltonian cycles. The central goal in multiobjective optimization and in the first part of this thesis in particular is the approximation of such Pareto sets. We first develop improved approximation algorithms for the two-objective metric traveling salesman problem on multigraphs and for related Hamiltonian path problems that are inspired by the single-objective Christofides' heuristic. We further show arguments indicating that our algorithms are difficult to improve. Furthermore we consider multiobjective maximization versions of the traveling salesman problem, where the task is to find Hamiltonian cycles with high weight in each objective. We generalize single-objective techniques to the multiobjective case, where we first compute a cycle cover with high weight and then remove an edge with low weight in each cycle. Since weight vectors are often incomparable, the choice of the edges of low weight is non-trivial. We develop a general lemma that solves this problem and enables us to generalize the single-objective maximization algorithms to the multiobjective case. We obtain improved, randomized approximation algorithms for the multiobjective maximization variants of the traveling salesman problem. We conclude the first part by developing deterministic algorithms for these problems. The second part of this thesis deals with redundancy properties of complete sets. We call a set autoreducible if for every input instance x we can efficiently compute some y that is different from x but that has the same membership to the set. If the set can be split into two equivalent parts, then it is called weakly mitotic, and if the splitting is obtained by an efficiently decidable separator set, then it is called mitotic. For different reducibility notions and complexity classes, we analyze how redundant its complete sets are. Previous research in this field concentrates on polynomial-time computable reducibility notions. The main contribution of this part of the thesis is a systematic study of the redundancy properties of complete sets for typical complexity classes and reducibility notions that are computable in logarithmic space. We use different techniques to show autoreducibility and mitoticity that depend on the size of the complexity class and the strength of the reducibility notion considered. For small complexity classes such as NL and P we use self-reducible, complete sets to show that all complete sets are autoreducible. For large complexity classes such as PSPACE and EXP we apply diagonalization methods to show that all complete sets are even mitotic. For intermediate complexity classes such as NP and the remaining levels of the polynomial-time hierarchy we establish autoreducibility of complete sets by locally checking computational transcripts. In many cases we can show autoreducibility of complete sets, while mitoticity is not known to hold. We conclude the second part by showing that in some cases, autoreducibility of complete sets at least implies weak mitoticity. / Der erste Teil dieser Arbeit widmet sich der Approximierbarkeit des Traveling Salesman Problems, bei welchem man in vollständigen Graphen mit Kantengewichten eine Rundreise mit minimalem Gewicht sucht. Es werden die wichtigsten mehrkriteriellen Varianten dieses Problems betrachtet, bei denen die Kantengewichte aus Vektoren natürlicher Zahlen mit einer Komponente pro Kriterium bestehen. Verschiedene Rundreisen sind bei mehrkriteriellen Kantengewichten häufig unvergleichbar, und dementsprechend existiert oft keine eindeutige optimale Rundreise. Stattdessen fasst man jene Rundreisen, zu denen jeweils keine eindeutig bessere Rundreise existiert, zu der sogenannten Pareto-Menge zusammen. Die Approximation solcher Pareto-Mengen ist die zentrale Aufgabe in der mehrkriteriellen Optimierung und in diesem Teil der Arbeit. Durch Techniken, die sich an Christofides' Heuristik aus der einkriteriellen Approximation orientieren, werden zunächst verbesserte Approximationsalgorithmen für das zweikriterielle metrische Traveling Salesman Problem auf Multigraphen und für analog definierte Hamiltonpfadprobleme entwickelt. Außerdem werden Argumente gegen eine signifikante Verbesserung dieser Algorithmen aufgezeigt. Weiterhin werden mehrkriterielle Maximierungsvarianten des Traveling Salesman Problems betrachtet, bei denen man Rundreisen mit möglichst großem Gewicht in jedem Kriterium sucht. Es werden einkriterielle Techniken auf den mehrkriteriellen Fall übertragen, bei denen man zunächst eine Kreisüberdeckung mit hohem Gewicht berechnet und anschließend pro Kreis die Kante mit dem niedrigsten Gewicht löscht. Die Auswahl einer solchen Kante pro Kreis ist im mehrkriteriellen Fall nicht trivial, weil mehrkriterielle Gewichtsvektoren häufig unvergleichbar sind. Es wird ein allgemeines Lemma entwickelt, welches dieses Problem löst und damit eine Übertragung der einkriteriellen Maximierungsalgorithmen auf den mehrkriteriellen Fall ermöglicht. Dadurch ergeben sich verbesserte, randomisierte Approximationsalgorithmen für die mehrkriteriellen Maximierungsvarianten des Traveling Salesman Problems. Abschließend werden zu diesen Problemvarianten deterministische Algorithmen entwickelt. Der zweite Teil dieser Arbeit widmet sich Redundanzeigenschaften vollständiger Mengen. Eine Menge heißt autoreduzierbar, wenn zu jeder Instanz x eine von x verschiedene Instanz y mit der gleichen Zugehörigkeit zu der Menge effizient berechnet werden kann. Ist die Menge in zwei äquivalente Teile aufspaltbar, so heißt sie schwach mitotisch, und ist diese Aufspaltung durch einen effizient entscheidbaren Separator erreichbar, so heißt sie mitotisch. Zu verschiedenen Reduktionen und Komplexitätsklassen wird die Frage betrachtet, wie redundant ihre vollständigen Mengen sind. Während sich vorherige Forschung in diesem Gebiet hauptsächlich auf Polynomialzeitreduktionen konzentriert, liefert diese Arbeit eine systematische Analyse der Redundanzeigenschaften vollständiger Mengen für typische Komplexitätsklassen und solche Reduktionen, die sich in logarithmischem Raum berechnen lassen. Je nach Größe der Komplexitätsklasse und Stärke der Reduktion werden dabei verschiedene Techniken eingesetzt. Für kleine Komplexitätsklassen wie beispielsweise NL und P werden selbstreduzierbare, vollständige Mengen benutzt, um Autoreduzierbarkeit aller vollständigen Mengen nachzuweisen, während für große Komplexitätsklassen wie beispielsweise PSPACE und EXP Diagonalisierungsmethoden sogar die Mitotizität vollständiger Mengen zeigen. Für dazwischen liegende Komplexitätsklassen wie beispielsweise NP und die übrigen Level der Polynomialzeithierarchie wird Autoreduzierbarkeit vollständiger Mengen über lokales Testen von Berechnungstranskripten gezeigt. Während in vielen Fällen Autoreduzierbarkeit vollständiger Mengen gezeigt werden kann, bleibt häufig die Frage offen, ob diese Mengen auch mitotisch sind. Abschließend wird gezeigt, dass in einigen Fällen Autoreduzierbarkeit vollständiger Mengen zumindest schwache Mitotizität impliziert.

Traveling salesman problem (TSP) is a well-known NP-hard problem which can not be solved within a polynomial bounded computation time. However, genetic algorithm (GA) is a familiar heuristic algorithm to obtain near-optimal solutions within reasonable time for TSPs. In TSPs, the geometric properties are problem specific knowledge can be used to enhance GAs. Some tour segments (edges) of TSPs are fine while some maybe too long to appear in a short tour. Therefore, this information can help GAs to pay more attention to fine tour segments and without considering long tour segments as often. Consequently, we propose a new algorithm, called intelligent-OPT hybrid genetic algorithm (IOHGA), to exploit local optimal tour segments and enhance the searching process in order to reduce the execution time and improve the quality of the offspring. The local optimal tour segments are assigned higher priorities for the selection of tour segments to be appeared in a short tour. By this way, tour segments of a TSP are divided into two separate sets. One is a candidate set which contains the candidate fine tour segments and the other is a non-candidate set which contains non-candidate fine tour segments. According to the priorities of tour segments, we devise two genetic operators, the skewed production (SP) and the fine subtour crossover (FSC). Besides, we combine the traditional GA with 2-OPT local search algorithm but with some modifications. The modified 2-OPT is named the intelligent OPT (IOPT). Simulation study was conducted to evaluate the performance of the IOHGA. The experimental results indicate that generally the IOHGA could obtain near-optimal solutions with less time and higher accuracy than the hybrid genetic algorithm with simulated annealing algorithm and the genetic algorithm using the gene expression algorithm. Thus, the IOHGA is an effective algorithm for solving TSPs. If the case is not focused on the optimal solution, the IOHGA can provide good near-optimal solutions rapidly. Therefore, the IOHGA could be incorporated with some clustering algorithm and applied to mobile agent planning problems (MAP) in a real-time environment.

Dynamic Programming is an optimization technique used in computer science and mathematics. Introduced in the 1950s, it has been applied to many classic combinatorial optimization problems, such as the Shortest Path Problem, the Knapsack Problem, and the Traveling Salesman Problem, with varying degrees of practical success. In this thesis, we present two applications of dynamic programming to optimization problems. The first application is as a method to compute the Branch-Cut-and-Price (BCP) family of lower bounds for the Traveling Salesman Problem (TSP), and several vehicle routing problems that generalize it. We then prove that the BCP family provides a set of lower bounds that is at least as strong as the Approximate Linear Program (ALP) family of lower bounds for the TSP. The second application is a novel dynamic programming model used to determine the placement of cuts for a particular form of skull surgery called Cranial Vault Remodeling.

University of Macau / Faculty of Science and Technology / Department of Mathematics

One of the simplest, but still NP-hard, routing problems is the Traveling Salesman Problem (TSP). In the TSP, one is given a set of cities and a way of measuring the distance between cities. One has to find the shortest tour that visits all cities exactly once and returns back to the starting city. In state-of-the-art algorithms, they all assume that a complete graph is given as an input. However, for very large graphs, generating all edges in a complete graph, which corresponds to finding shortest paths for all city pairs, could be time-consuming. This is definitely a major obstacle for some real-life applications, especially when the tour needs to be generated in real-time. The objective, in this thesis, is to find a near-optimal TSP tour with a reduced set of edges in the complete graph. In particular, the following problems are investigated: which subset of edges can be produced in a shorter time comparing to the time for generating the complete graph? Is there a subset of edges in the complete graph that results in a better near-optimal tour than other sets? With a non-complete graph, which improvement algorithms work better? In this thesis, we study six algorithms to generate subsets of edges in a complete graph. To evaluate the proposed algorithms, extensive experiments are conducted with the well-known TSP data in a TSP library. In these experiments, we evaluate these algorithms in terms of tour quality, time and scalability.

This thesis is a survey on the approximability of the asymmetric traveling salesmanproblem with triangle inequality (ATSP).In the ATSP we are given a set of cities and a function that gives the cost of travelingbetween any pair of cities. The cost function must satisfy the triangle inequality, i.e.the cost of traveling from city A to city B cannot be larger than the cost of travelingfrom A to some other city C and then to B. However, we allow the cost function tobe asymmetric, i.e. the cost of traveling from city A to city B may not equal the costof traveling from B to A. The problem is then to find the cheapest tour that visit eachcity exactly once. This problem is NP-hard, and thus we are mainly interested in approximationalgorithms. We study the repeated cycle cover heuristic by Frieze et al. We alsostudy the Held-Karp heuristic, including the recent result by Asadpour et al. that givesa new upper bound on the integrality gap. Finally we present the result ofPapadimitriou and Vempala which shows that it is NP-hard to approximate the ATSP with a ratio better than 117/116.