Singular value decomposition and least squares solutions

@article{Golub2007SingularVD,
  title={Singular value decomposition and least squares solutions},
  author={Gene H. Golub and Christian H. Reinsch},
  journal={Numerische Mathematik},
  year={2007},
  volume={14},
  pages={403-420}
}
Let A be a real m×n matrix with m≧n. It is well known (cf. [4]) that $$A = U\sum {V^T}$$ (1) where $${U^T}U = {V^T}V = V{V^T} = {I_n}{\text{ and }}\sum {\text{ = diag(}}{\sigma _{\text{1}}}{\text{,}} \ldots {\text{,}}{\sigma _n}{\text{)}}{\text{.}}$$ The matrix U consists of n orthonormalized eigenvectors associated with the n largest eigenvalues of AA T , and the matrix V consists of the orthonormalized eigenvectors of A T A. The diagonal elements of ∑ are the non-negative… 

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