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This thesis presents and evaluates a generic algorithm for incrementally computing the dominant singular subspaces of a matrix. The relationship between the generality of the results and the necessary computation is explored. The performance of this method, both numerical and computational, is discussed in terms of the algorithmic parameters, such as block size and acceptance threshhold. Bounds on the error are presented along with a posteriori approximations of these bounds. Finally, a group of methods are proposed which iteratively improve the accuracy of computed results and the quality of the bounds.
Updating, Numerical Linear Algebra, Singular Value Decomposition, URV Factorization, Subspace Tracking
Date of Defense
Date of Defense: April 19, 2004.
A Thesis submitted to the Department of Computer Science in partial fulﬁllment of the requirements for the degree of Master of Science.
Includes bibliographical references.
Florida State University
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