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Exact unitary equivalence of Householder bidiagonalization
Statement
For over or , the exact-arithmetic algorithm Householder bidiagonalization and its finite precision status returns unitary and an upper bidiagonal , , such that if then , and if then .
Facts & Assumptions
Given: A matrix and the exact-arithmetic Householder bidiagonalization algorithm.
Householder bidiagonalization and its finite precision status applies a left Householder to the active column at step and, when , a right Householder to the conjugate transpose of the active row after column . For each nonzero active vector , it chooses when and otherwise, , , , and . A zero vector uses the identity.
Householder reflectors and Givens transformations are unitary and can annihilate prescribed entries: every Householder reflector with a unit normal is unitary.
Proof
For any nonzero active vector , [F1] specifies , if and otherwise, , , and . Since , direct expansion gives and . Thus , , and satisfies and . It is unitary by [F2]. For the specified identity has the same zero-annihilation property. This verifies the actual negative-phase construction on both the column vector and the conjugate-transposed row vector.
At step , the left reflector acts only on rows . It therefore leaves all rows unchanged and maps the active part of column to its first coordinate, making entries below zero. The right reflector acts only on columns , so it preserves the zeros already made in column and maps the active part of row to its first coordinate, making entries beyond zero.
Inductively, before stage , each earlier column has zeros in rows , so the left update preserves those zeros. Each earlier row has zeros in columns , so the right update preserves those zeros as well; neither update touches its earlier retained entries. Step 2.1 supplies the new column and row zeros. At the last stage, column also has zeros below row . Induction therefore gives an upper bidiagonal leading block and zero bottom rows. For the work matrix is already the empty tall matrix and both initialized factors are identities.
The update rules in [F1] maintain the invariant , where is the initial tall-oriented matrix. Every factor is unitary by [F2], so the accumulated are unitary. Combining this invariant with step 3.1 gives the asserted tall reduction. In the wide case it applies to , and taking adjoints gives .
Depends on
Used by
- Finite budget implicit shifted QR for bidiagonal singular values Algorithm
- Rank-thresholded least squares by the singular value decomposition Algorithm
- AAA recovers a scalar transfer-function pole from four samples Example
- Local stable reflectors and rotations give an aggregate SVD backward error Theorem
Dependency tree · two levels
7 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Wen-Wei Lin, Lecture Notes of Matrix Computations, §3.2.2, Algorithm 3.2.6 (standard reference, not scraped)
- James Demmel and W. Kahan, Accurate Singular Values of Bidiagonal Matrices, §1 (standard reference, not scraped)