pith:HKF5WRBM
A Riemannian gradient descent method for optimization on the indefinite Stiefel manifold
A Riemannian gradient descent method on the indefinite Stiefel manifold X^T A X = J converges globally to critical points.
arxiv:2410.22068 v3 · 2024-10-29 · math.OC
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Claims
We ... suggest a Riemannian gradient descent method using the attained materials, whose global convergence is guaranteed. Our results not only cover the known cases, the orthogonal and generalized Stiefel manifolds, but also provide a Riemannian optimization solution for other constrained problems which has not been investigated.
The feasible set X^T A X = J constitutes a differentiable manifold (the indefinite Stiefel manifold) that admits a Riemannian metric allowing construction of the associated geometric structure and a well-defined Cayley retraction.
Develops Riemannian gradient descent with Cayley retraction on the indefinite Stiefel manifold X^T A X = J, proves global convergence, generalizes orthogonal cases, and applies to eigenvalue problems and Procrustes-type matrix equations.
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| First computed | 2026-05-26T01:03:09.536728Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
3a8bdb442ceeccf07e17d28a26e15bef4662b5875dece7b12a0444faabede767
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/HKF5WRBM53GPA7QX2KFCNYK355 \
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Canonical record JSON
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