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.
Real Stiefel and Grassmann optimization conventions
Definition
For integers , the real Stiefel manifold is The real Grassmannian is the set of -dimensional linear subspaces of , identified with the quotient of by the right action of . A frame represents the subspace .
On matrix spaces use the Frobenius inner product . Give Stiefel its induced metric. On the Grassmann quotient use the quotient metric: identify a tangent vector at with its unique horizontal representative satisfying , and pair horizontal representatives by the Frobenius inner product. A Grassmann formula written with a frame representative must be unchanged under for .
For a square real matrix , set .
Facts & Assumptions
Given: Integers and the real matrix spaces with their Euclidean topology.
The right action of on orthonormal frames changes frame but not the represented subspace, and its quotient is the Grassmannian (Stiefel spaces, Grassmannians, and tautological bundles).
A Riemannian metric is a smooth symmetric positive-definite bilinear form on each tangent space (Riemannian metric and riemannian manifold).
The Euclidean inner product on a finite real coordinate space is the sum of coordinatewise products (The Euclidean inner product on ).
A Grassmannian point is an -dimensional subspace of the ambient vector space (The Grassmannian of r-dimensional subspaces of a finite-dimensional vector space).
Proof
For any real matrix , , so this pairing is symmetric bilinear and positive for every nonzero by [F3]. Its restriction to each tangent or horizontal subspace is therefore a positive-definite inner product, as required by [F2].
In a local Grassmann graph chart, choose a smooth orthonormal frame section from the local frame-bundle trivialization in [F1], and set . For a chart tangent , differentiating gives skew-symmetric. The first term is vertical: for every skew-symmetric , the Cayley curve satisfies near zero and . Thus is the unique horizontal representative of . Then . For horizontal , direct expansion gives . Thus the quotient pairing has smooth coefficients in every graph chart. In a local product chart of the frame bundle, matrix entries vary smoothly, so the ambient Frobenius pairing has smooth coefficients on Stiefel as well.
By [F4], each frame represents an -dimensional subspace, and [F1] identifies frames representing the same subspace by the right orthogonal action. For , cyclicity of trace gives . Thus the action is isometric and the horizontal pairing is independent of the chosen orthonormal frame.
Depends on
Used by
- Finite-budget retraction Armijo backtracking Algorithm
- Finite-budget Riemannian trust-region method Algorithm
- Projected vector transport on Stiefel and Grassmann Definition
- QR and polar retractions on Stiefel and Grassmann Definition
- A repeated eigenvalue gives a continuum of Grassmann minimizers Example
- A strict saddle of a quadratic Stiefel objective Example
- A trace eigenspace objective and a Lanczos start-angle bound Example
- Complex finite-basis PC-VAR optimization on Grassmann Example
- Finite-dimensional Stiefel and Grassmann manifolds are compact Proposition
- Projected Euclidean gradients are the Stiefel and Grassmann gradients Proposition
- Tangent spaces and Frobenius projections on Stiefel and Grassmann Theorem
Dependency tree · two levels
24 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 3 (2008) (standard reference, not scraped)
- Nicolas Boumal, An Introduction to Optimization on Smooth Manifolds (2023 prepublication) (standard reference, not scraped)