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The truncated SVD as a method for regularization
Stanford, CA, USA, (1986)
Аннотация
The truncated singular value decomposition (SVD) is considered as a method for regularization of ill-posed linear least squares problems. In particular, the truncated SVD solution is compared with the usual regularized solution. Necessary conditions are defined in which the two methods will yield similar results. This investigation suggests the truncated SVD as a favorable alternative to standard-form regularization in case of ill-conditioned matrices with a well-determined rank.
