Rank-Deficient and Discrete Ill-Posed Problems: Numerical by Per Christian Hansen

By Per Christian Hansen

This is an outline of contemporary computational stabilization equipment for linear inversion, with functions to a number of difficulties in audio processing, scientific imaging, seismology, astronomy, and different components. Rank-deficient difficulties contain matrices which are precisely or approximately rank poor. Such difficulties frequently come up in reference to noise suppression and different difficulties the place the aim is to suppress undesirable disturbances of given measurements. Discrete ill-posed difficulties come up in reference to the numerical remedy of inverse difficulties, the place one as a rule desires to compute information regarding inside houses utilizing external measurements. Examples of inverse difficulties are snapshot recovery and tomography, the place one must increase blurred photos or reconstruct images from uncooked information. This publication describes new and present numerical equipment for the research and resolution of rank-deficient and discrete ill-posed difficulties. The emphasis is on perception into the stabilizing houses of the algorithms and the potency and reliability of the computations.

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Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion

Here's an outline of contemporary computational stabilization tools for linear inversion, with functions to various difficulties in audio processing, scientific imaging, seismology, astronomy, and different components. Rank-deficient difficulties contain matrices which are precisely or approximately rank poor. Such difficulties usually come up in reference to noise suppression and different difficulties the place the objective is to suppress undesirable disturbances of given measurements.

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TRANSFORMATION TO STANDARD FORM 41 Hence, since AK0T~1H^ — H0H^ we obtain When we insert these quantities into the residual norm \\Ax — b\\2 we see that the leftmost factor Hq does not contribute to the 2-norms. , the standard-form quantities become This is the form used in [98]. 41) are computed. 2. Implicit Transformation For iterative methods, where A is accessed only via matrix-vector products with A and AT, it is not practical to form A or A' explicitly. 43) and use an implicit standard-form transformation.

The remaining six vectors shown in Fig. 2 correspond to the six largest generalized singular values. 2, the generalized singular vectors are very similar to the ordinary singular vectors. The oscillation properties also carry over to the generalized singular vectors v^ which are not shown here. 4. Other SVDs After the introduction of the GSVD (or QSVD), it took some years before other generalizations of the SVD were introduced. , the SVD of ALT is made up of matrices from the PSVD. , in connection with the orthogonal Procrustes problem: given A and B, the orthogonal matrix Q that minimizes \\A — B Q\\F is given by Q — U VT where BTA = UY> VT (see also [205]).

However, we are interested in alternative decompositions that can reveal the numerical rank of A at a smaller computational cost than that involved in 5 Updating schemes for an "approximate SVD" are developed in [252], [253]. In these schemes the diagonal matrices E and M are replaced by triangular matrices whose elements decay rapidly in magnitude away from the diagonal. 2. RANK-REVEALING DECOMPOSITIONS 31 computing the SVD. Before turning our attention to the rank-revealing decompositions, it is instructive to consider the most basic problem in connection with rank-deficient matrices: is a given matrix A rank deficient?

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