by Chan Chiu Fat. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 99-100). / Acknowledgment --- p.i / Abstract --- p.ii / Introduction --- p.1 / Chapter 1 --- Differentiability On Banach Spaces --- p.4 / Chapter 1.1 --- Introduction --- p.5 / Chapter 1.2 --- Subdifferentiability of Convex Functions --- p.12 / Chapter 1.3 --- Generalized Gradients --- p.16 / Chapter 1.4 --- The Rockafellar Subderivatives --- p.21 / Chapter 2 --- On Minimizing and Stationary Sequences --- p.25 / Chapter 2.1 --- On Equivalence of Minimizing and Stationary Sequences --- p.26 / Chapter 2.2 --- Constrained Optimization Problem --- p.35 / Chapter 2.3 --- Convergence of an Iterative Algorithm --- p.41 / Chapter 3 --- Asymptotically Weil-Behaved Functions --- p.47 / Chapter 3.1 --- Asymptotically Well-Behaved Convex Functions --- p.48 / Chapter 3.2 --- The Subclass R --- p.54 / Chapter 3.3 --- Application on Approximate Problems and Duality Theory --- p.61 / Chapter 4 --- The Minimum and Maximum Principle --- p.71 / Chapter 4.1 --- The Radon-Nikodym Property --- p.71 / Chapter 4.2 --- The Minimum and Maximum Principle for Nonconvex Functions --- p.83 / Bibliography --- p.99

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Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 100-104). Also available via the Internet.

Thesis (Ph.D.) -- University of Glasgow, 1981. / Includes bibliographical references (p. 329-336). Print version also available.

Thesis--Wisconsin. / Includes bibliographical references (leaves 101-102).

Thesis (Ph. D.)--University of Glasgow, 1998. / Print version also available.

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Thesis (M. Sc.)(Computer Science)--University of Pretoria, 2002. / Includes bibliographical references (p. 130-136).

M.Sc. (Computer Science) / Chapter 1 is a summary in which the problems- discussed in this study, as well as the relationship between them are shown. The algorithmic notation used when discussing the problems are also defined. In chapter 2 the three classes of algorithms for finding a minimum spanning tree, i.e. the algorithms of Prim, Kruskal and SolIin are discussed. PASCAL implementations of all the algorithms are presented. We also report on our computational experience with these implementations. It was found that the implementation of the Prim algorithm was very efficient, while implementations of the Kruskal algorithm also gave good results. Implementations of the Sollin algorithm were less efficient, because of the complex data structures involved. Algorithms for finding an optimum arborescence or branching in a network have independently been proposed by Edmonds, Chu & Liu as well as Bock. Tarjan as well as Camerini et al subsequently discussed aspects of the efficient implementation of the algorithm. In chapter 3 we draw attention to the related work of Fulkerson by reformulating the Edmonds-Fulkerson algorithm and giving a simple proof of the correctness of the algorithm. We also discuss and present an implementation of the algorithm and report on our computational experience. From the results presented it is clear that the number of cycles encountered during the first phase of the algorithm has a significant effect on the efficiency of the algorithm...