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If is a simple singular value with left and right singular vectors , then its real directional derivative is
Statement
Let be a matrix, let be a simple singular value of , and let be corresponding unit left and right singular vectors, so and . Then the real directional derivative of in the direction is
Facts & Assumptions
Given: A matrix , a simple positive singular value , unit singular vectors , and a perturbation direction .
For a Hermitian simple eigenvalue, the directional derivative is for the corresponding unit eigenvector (For a Hermitian simple eigenvalue, one may take and the first-order formulas simplify accordingly).
A simple eigenvalue of a differentiable matrix path admits a local eigenvalue branch after gauge fixing (A simple eigenvalue and a gauge-fixed right eigenvector admit local branches in the underlying real matrix space).
Proof
Form the Hermitian block path Then is a unit eigenvector of with eigenvalue , because If , then and , so . Since is a simple positive singular value, the eigenspace of for is one-dimensional, and then is determined by . Hence is a simple eigenvalue of the Hermitian matrix .
Because is differentiable and step 1.1 shows that is a simple eigenvalue of , [L2] gives a local eigenvalue branch through . The derivative of the block path is Applying [L1] to this Hermitian simple eigenvalue branch gives
Expanding the quadratic form from step 2.1 gives
Depends on
- The real Frechet derivative on real and complex matrix spaces with the Frobenius norm
- Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
- For a Hermitian simple eigenvalue, one may take $y=x$ and the first-order formulas simplify accordingly
- A simple eigenvalue and a gauge-fixed right eigenvector admit local $C^1$ branches in the underlying real matrix space
Used by
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Sources
- Alan Edelman and Steven G. Johnson, Matrix Calculus for Machine Learning and Beyond (standard reference, not scraped)