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QR and polar retractions on Stiefel and Grassmann
Definition
For and , put . On the domain where has full column rank, let be the factor in the unique reduced QR factorization with upper triangular and positive diagonal. Define the QR map by . Define the polar map by where the exponent denotes the inverse of the unique positive-definite square root. For a Grassmann tangent at , use its unique horizontal representative satisfying and define the induced map by taking the output column space. Under , this representative changes to , so the input matrix changes to . The polar formula is right-equivariant: for every .
These are defined only where the displayed input is full rank. In fact, for a Stiefel tangent , always has full column rank, since for every .
Facts & Assumptions
Given: and as in Tangent spaces and Frobenius projections on Stiefel and Grassmann.
Gram–Schmidt orthonormalization gives an orthonormal basis from a linearly independent ordered family (Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans).
A real symmetric matrix has an orthonormal eigenbasis (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis).
Every positive-definite self-adjoint bundle endomorphism has a unique smooth positive square root (Positive-definite bundle endomorphisms have smooth positive square roots).
A reduced QR factorization writes a full-column-rank matrix as an orthonormal-column factor times an upper-triangular factor (Full, reduced, and column-pivoted computational QR factorisations).
A tangent matrix satisfies (Tangent spaces and Frobenius projections on Stiefel and Grassmann).
A Grassmann tangent has a unique horizontal representative, and changing the frame from to changes it from to (Real Stiefel and Grassmann optimization conventions).
Proof
By [F5], tangency gives . Therefore for , Thus has full column rank. More generally, for any full-rank , for , so its Gram matrix is positive definite; [F1] supplies the QR factor, while [F2] diagonalizes the Gram matrix and [F3] supplies its positive square root and inverse.
For full-column-rank , its columns are linearly independent. Apply Gram–Schmidt [F1], writing and . The residuals are nonzero, so has orthonormal columns. Set . For , is orthogonal to , so ; and . Since each lies in , . This constructs a reduced QR factorization with positive diagonal. To check uniqueness, if are two such factorizations, then is orthogonal and upper triangular with positive diagonal. Its first column has only its first entry, which must be by unit norm and positivity; orthogonality zeros the rest of its first row. Induction on the remaining block makes it , so and . For the polar factor , symmetry of the root and its defining square give . Hence both outputs have orthonormal columns. For a Grassmann input, [F6] says a frame change changes to , so the input becomes with the same column space; uniqueness of the positive root gives , so the polar output is multiplied by . Thus the Grassmann outputs are well-defined.
Remark
The formulas here are exact-arithmetic definitions. The finite-precision Householder QR supplier proves a backward-error statement under its operationwise assumptions and does not assert that a computed is exactly orthogonal (The scaled Householder QR implementation is backward stable). No floating-point retraction guarantee is claimed by this page.
Depends on
- Real Stiefel and Grassmann optimization conventions
- Tangent spaces and Frobenius projections on Stiefel and Grassmann
- Full, reduced, and column-pivoted computational QR factorisations
- Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans
- Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis
- Positive-definite bundle endomorphisms have smooth positive square roots
- The scaled Householder QR implementation is backward stable
Used by
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Sources
- P.-A. Absil, R. Mahony, and R. Sepulchre, Optimization Algorithms on Matrix Manifolds, Chapter 4 (2008) (standard reference, not scraped)
- Nicolas Boumal, An Introduction to Optimization on Smooth Manifolds (2023 prepublication) (standard reference, not scraped)