A quasi-Grassmannian gradient flow model for eigenvalue problems
arXiv:2506.20195
The paper introduces a quasi‑Grassmannian gradient flow model for solving eigenvalue problems of linear operators, which naturally enforces orthogonality over time and converges exponentially to the true eigenpairs without requiring orthogonal initial data.
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.