Math 2.0

Linear and multilinear algebra; matrix theory Canonical forms and decompositions Theorem

Singular value decomposition

Builds on Singular valueUnitary matrix

Statement

Let AA be an m×nm \times n complex matrix. Then there are a unitary matrix UU of size m×mm \times m, a unitary matrix VV of size n×nn \times n, and an m×nm \times n matrix Σ\Sigma with A=UΣV∗.A = U \Sigma V^*. Here Σij=0\Sigma_{ij} = 0 for i≠ji \ne j, and Σ11≥Σ22≥⋯≥0\Sigma_{11} \ge \Sigma_{22} \ge \cdots \ge 0 are the singular values of AA.

Remarks

If AA is real, then UU and VV can be chosen real orthogonal. The columns of VV are eigenvectors of A∗AA^* A, and the columns of UU are eigenvectors of AA∗A A^*. The matrix Σ\Sigma is unique; UU and VV are not unique in general. Keeping the kk largest singular values gives the best approximation of AA of rank kk, for the Frobenius and operator norms.

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  1. Identity matrix object
  2. Matrix definition
  3. Vector space definition
  4. Square matrix definition
  5. Transpose definition
  6. Linear map definition
  7. Conjugate transpose definition
  8. Endomorphism definition
  9. Hermitian matrix definition
  10. Unitary matrix definition
  11. Eigenvalue definition
  12. Positive semidefinite matrix definition
  13. Gram matrices are positive semidefinite statement
  14. Singular value definition
  15. Singular value decomposition statement

Where this leads

3 pages build on this one.

Further reading

  1. Web https://en.wikipedia.org/wiki/Singular_value_decomposition, lead section
About this page
Tag000NPTMX6J
URIm2://000NPTMX6J
Kindstatement, theorem
WritingW1 stated
MSC15A18, 15A23
StatusThis copy of the site has no build data. Run m2 status 000NPTMX6J.
UsesSingular value, Unitary matrix
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