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.
Tangent spaces and Frobenius projections on Stiefel and Grassmann
Statement
For , the tangent space and its Frobenius orthogonal projection are For , its tangent vectors have unique horizontal representatives with , and the quotient-metric projection of an ambient to this horizontal space is
Facts & Assumptions
Given: , , and the Frobenius metrics of Real Stiefel and Grassmann optimization conventions.
The Stiefel constraint map is , and the Grassmannian is the quotient of orthonormal frames by the right orthogonal action (Stiefel spaces, Grassmannians, and tautological bundles).
At a regular level, the tangent space is the kernel of the differential (The tangent space of a regular level set is the kernel).
Proof
The derivative of at is . It is onto the symmetric matrices because for any symmetric , the choice gives . Thus is a regular value; [F2] identifies the tangent space with the displayed kernel.
Write . Then , so is tangent. For every symmetric and tangent , because is skew-symmetric. Hence is normal and is the orthogonal projection.
A curve in through has skew-symmetric by differentiating . Conversely, for any skew-symmetric , the Cayley curve is defined near zero, satisfies , and has ; hence the vertical tangent directions are exactly . Every Stiefel tangent decomposes as , where is skew and the second term is horizontal. These summands are orthogonal, so the quotient tangent is represented uniquely by the horizontal summand.
The matrix is horizontal. For every horizontal , , since . Therefore it is the quotient-metric projection. Under and it becomes , the same tangent vector in the changed representative.
Depends on
Used by
- 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
- Projected Euclidean gradients are the Stiefel and Grassmann gradients Proposition
- Projected vector transports return tangent representatives Proposition
- The QR and polar maps are first-order Stiefel and Grassmann retractions 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
- 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)