How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
The QR and polar maps are first-order Stiefel and Grassmann retractions
Statement
On the full-column-rank domains of QR and polar retractions on Stiefel and Grassmann, the positive-diagonal QR and polar maps are smooth, take values in , fix , and have differential . Their induced Grassmann maps are independent of the representative and have identity differential under the horizontal tangent identification. Thus they are first-order retractions. This theorem makes no second-order claim; in particular, the QR map need not be second order.
Facts & Assumptions
Given: , the full-rank formulas of QR and polar retractions on Stiefel and Grassmann, and .
The retraction maps are the positive-diagonal QR factor and the polar factor of (QR and polar retractions on Stiefel and Grassmann).
Tangency is characterized by (Tangent spaces and Frobenius projections on Stiefel and Grassmann).
Gram–Schmidt constructs an orthonormal basis on the full-rank domain (Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans).
The positive square root depends smoothly on a positive-definite matrix (Positive-definite bundle endomorphisms have smooth positive square roots).
Matrix inversion preserves regularity on the nonvanishing-determinant domain (Matrix inversion preserves regularity where the determinant is nonzero).
Proof
On the full-rank set, the Gram–Schmidt recurrences divide only by positive residual norms, so [F3] expresses the QR factors by smooth additions, products, divisions by nonzero functions, and positive square roots. The polar formula is smooth there by [F4], [F5] applied to its positive-definite Gram matrix, and smooth matrix multiplication. Both maps land in Stiefel by [F1]. At , the factorizations are , so each map returns .
For the polar map, by [F2]. Its inverse square root equals by smoothness in [F4] and [F5] and its value at . Consequently , so its derivative at zero is .
Set and write its positive-diagonal QR factorization as . By step 1.1, are differentiable near zero; , . Put and , so and is upper triangular. Differentiating shows is skew-symmetric. By [F2], is skew-symmetric; hence is skew-symmetric as well. A real matrix both upper triangular and skew-symmetric is zero, so and .
Under , the inputs and have the same column space. Their QR outputs may differ as frames, but induce the same Grassmann point; the polar output transforms by . Differentiating the induced maps therefore gives the same horizontal class of , which is the identity on the quotient tangent space.
Depends on
- QR and polar retractions on Stiefel and Grassmann
- Tangent spaces and Frobenius projections on Stiefel and Grassmann
- Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans
- Positive-definite bundle endomorphisms have smooth positive square roots
- Matrix inversion preserves $C^k$ regularity where the determinant is nonzero
Used by
- Finite-budget retraction Armijo backtracking Algorithm
- Finite-budget Riemannian trust-region method Algorithm
- Projected vector transport on Stiefel and Grassmann Definition
- Complex finite-basis PC-VAR optimization on Grassmann Example
- Retraction trust-region ratios are accurate at small radii Lemma
- Projected vector transports return tangent representatives Proposition
- Uniform pullback smoothness guarantees an Armijo step Theorem
Dependency tree · two levels
23 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
- 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)