Using interior point algorithms for the solution of linear programs with special structural features

Linear Programming (LP) is a powerful decision making tool extensively used in various economic and engineering activities. In the early stages the success of LP was mainly due to the efficiency of the simplex method. After the appearance of Karmarkar's paper, the focus of most research was shifted to the field of interior point methods. The present work is concerned with investigating and efficiently implementing the latest techniques in this field taking sparsity into account. The performance of these implementations on different classes of LP problems is reported here. The preconditional conjugate gradient method is one of the most powerful tools for the solution of the least square problem, present in every iteration of all interior point methods. The effect of using different preconditioners on a range of problems with various condition numbers is presented. Decomposition algorithms has been one of the main fields of research in linear programming over the last few years. After reviewing the latest decomposition techniques, three promising methods were chosen the implemented. Sparsity is again a consideration and suggestions have been included to allow improvements when solving problems with these methods. Finally, experimental results on randomly generated data are reported and compared with an interior point method. The efficient implementation of the decomposition methods considered in this study requires the solution of quadratic subproblems. A review of recent work on algorithms for convex quadratic was performed. The most promising algorithms are discussed and implemented taking sparsity into account. The related performance of these algorithms on randomly generated separable and non-separable problems is also reported.

Insect pest control research : the analysis of historical trends with special reference to scientometric analysis

The thesis investigates the value of quantitative analyses for historical studies of science through an examination of research trends in insect pest control, or economic entomology. Reviews are made of quantitative studies of science, and historical studies of pest control. The methodological strengths and weaknesses of bibliometric techniques are examined in a special chapter; techniques examined include productivity studies such as paper counts, and relational techniques such as co-citation and co-word analysis. Insect pest control is described. This includes a discussion of the socio-economic basis of the concept of `pest'; a series of classifications of pest control techniques are provided and analysed with respect to their utility for scientometric studies. The chemical and biological approaches to control are discussed as scientific and technological paradigms. Three case studies of research trends in economic entomology are provided. First a scientometric analysis of samples of chemical control and biological control papers; providing quantitative data on institutional, financial, national, and journal structures associated with pest control research fields. Second, a content analysis of a core journal, the Journal of Economic Entomology, over a period of 1910-1985; this identifies the main research innovations and trends, in particular the changing balance between chemical and biological control. Third, an analysis of historical research trends in insecticide research; this shows the rise, maturity and decline of research of many groups of compounds. These are supplemented by a collection of seven papers on scientometric studies of pest control and quantitative techniques for analysing science.