A quasi-Grassmannian gradient flow model for eigenvalue problems

Shengyue Wang; Aihui Zhou

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arXiv · 2506.20195

A quasi-Grassmannian gradient flow model for eigenvalue problems

Shengyue Wang·Aihui Zhou

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Abstract

We propose a quasi-Grassmannian gradient flow model for eigenvalue problems of linear operators, aiming to efficiently address many eigenpairs. Our model inherently ensures asymptotic orthogonality: without the need for initial orthogonality, the solution naturally evolves toward being orthogonal over time. We establish the well-posedness of the model, and provide the analytic representation of solutions. Through asymptotic analysis, we show that the gradient converges exponentially to zero and that the energy converges exponentially to its minimum. This implies that the solution of the quasi-Grassmannian gradient flow model converges to the solution of the eigenvalue problems as time progresses. These results provide a continuous-flow framework in which the Stiefel constraint is recovered asymptotically rather than imposed on the initial data.

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Shengyue Wang, Aihui Zhou. 2025-06-25. A quasi-Grassmannian gradient flow model for eigenvalue problems. https://arxiv.org/abs/2506.20195

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