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discrete optimization models in data visualization

dc.contributor.advisorGolden, Bruceen_US
dc.contributor.authorAbbiw-Jackson, Roselyn Mansaen_US
dc.description.abstractData visualization techniques have become important tools for analyzing large multidimensional data sets and providing insights with respect to scientific, economic, and engineering applications. Typically, these visualization applications are modeled and solved using nonlinear optimization techniques. In this dissertation, we propose a discretization of the data visualization problem that allows us to formulate it as a quadratic assignment problem. This formulation is computationally difficult to solve optimally using an exact approach. Consequently, we investigate the use of local search techniques, mathematical programming, and genetic algorithms for the data visualization problem. The space in which the data points are to be embedded can be discretized using an n x n lattice. Conducting a search on this n x n lattice is computationally ineffective. Consequently, we propose a divide-and-conquer approach that refines the lattice at each step. We show that this approach is much faster than conducting a search of the entire n x n lattice and, in general, it generates higher quality solutions. We envision two uses of our divide-and-conquer heuristics: (1) as stand-alone approaches for data visualization and (2) to provide good approximate starting solutions for a nonlinear algorithm.en_US
dc.format.extent2305499 bytes
dc.titlediscrete optimization models in data visualizationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.subject.pqcontrolledApplied Mechanicsen_US
dc.subject.pqcontrolledOperations Researchen_US

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