Prance to search shape Skip to duct content. This sets the stage in support of fast and memory-efficient sequential algorithms someone is concerned tracking singular values and subspaces. Appear in conjunction with a fast solution in place of the pseudo-inverse of a submatrix of an orthogonal environment, we develop a scheme for computing a thin SVD of streaming information in a distinguished pass with linear time complexity: Vision via Publisher.
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As one of the basic problems in matrix computation, rank-revealing arises in a wide variety of applications in scientific computing. Although the singular value decomposition is the standard rank-revealing method, it is costly in both computing time and storage when the rank or the nullity is low, and it is inefficient in updating and downdating when rows and columns are inserted or deleted. Consequently, alternative methods are in demand in those situations.
He has developed efficient algorithms for the singular value decomposition, updating and downdating matrix factorizations, and the.
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