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and are the orthogonal projections onto and
Statement
Let be a finite real or complex matrix. Then is the orthogonal projection onto , and is the orthogonal projection onto .
Facts & Assumptions
Given: A matrix over or .
Every finite real or complex matrix has a unique Moore--Penrose pseudoinverse (Every finite real or complex matrix has a unique Moore--Penrose pseudoinverse).
The rank equals the number of nonzero singular values (The rank of a linear map is the number of its nonzero singular values).
A self-adjoint idempotent is exactly an orthogonal projection onto its image (An endomorphism is an orthogonal projection exactly when it is idempotent and self-adjoint).
Every finite real or complex matrix has a singular value decomposition (Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition).
Proof
By [L4], write . Define by reciprocating the positive singular values and leaving the zero block fixed. Direct diagonal multiplication verifies the four Penrose equations, so uniqueness in [L1] gives . Hence
The products and are the respective rank- diagonal projections, each with in its nonzero singular directions and elsewhere, where is the number of nonzero singular values from [L2]. Hence and are self-adjoint idempotents.
By [L3], and are orthogonal projections onto their image spaces. In the singular basis, is the span of the left singular vectors corresponding to the nonzero singular values, which is exactly .
The same computation shows that is the span of the right singular vectors corresponding to the nonzero singular values, which is exactly .
Therefore and are the orthogonal projections onto and respectively.
Depends on
- The rank of a linear map is the number of its nonzero singular values
- Every finite real or complex matrix has a unique Moore--Penrose pseudoinverse
- An endomorphism is an orthogonal projection exactly when it is idempotent and self-adjoint
- Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
Used by
- The Moore--Penrose pseudoinverse exchanges image and adjoint-image, and exchanges kernel and adjoint-kernel Corollary
- A diagonal rank-deficient matrix makes A^+, AA^+, and A^+A completely explicit Example
- Every least-squares solution has the form A^+b+(I-A^+A)z, and the same affine family specializes to exact solutions when b inimA Theorem
- For every right-hand side b, A^+b is the unique least-squares solution of minimum Euclidean norm Theorem
Dependency tree · two levels
14 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
- Roger Penrose, A generalized inverse for matrices (standard reference, not scraped)
- Stanford CS205L, Unit 11: Zero Singular Values (standard reference, not scraped)