Updating the singular value decomposition

Since the updating require about 6n^(3) multiplications the algorithms are useful when m n.

The problem of recalculation some or all of the singular values of a matrix A', which is obtained by deleting or adding a row or a column from a matrix A, whose singular value decomposition is known, is also studied.

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The papers are in the public domain and are not subject to copyright in the United States.These methods are compared to an SVD update method known from the literature.The comparison criterion of interest is the theoretical computational complexity, it being understood that the dimension of the observation vectors is much larger than the number of observations.This work is a departure from most classical model updating work, which utilizes model data to update linear structural dynamics models.In the present application a singular value decomposition (SVD) of the measured data (e.g., m of the N coordinates are measured at n sampling times) is the basis of the updating.

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