From the 1 of 4 linked papers with an AI index.
4 papers
A quasi-Grassmannian gradient flow model for eigenvalue problems
Shengyue Wang, Aihui Zhou
The paper introduces a quasi‑Grassmannian gradient flow model for solving eigenvalue problems of linear operators, which naturally enforces orthogonality over time and converges ex…
A quasi-orthogonal method based on the inverse operator for Schr{ö}dinger eigenvalue problems
Shengyue Wang, Aihui Zhou
Computing many eigenpairs of the Schr{ö}dinger operator presents a computational bottleneck in large-scale quantum simulations due to the global communication overhead of explicit…
A quasi-orthogonal iterative method for eigenvalue problems
Shengyue Wang, Aihui Zhou
For large-scale eigenvalue problems requiring many mutually orthogonal eigenvectors, traditional numerical methods suffer substantial computational and communication costs with lim…
An orthogonality-preserving approach for eigenvalue problems
Tianyang Chu, Xiaoying Dai, Shengyue Wang +1
Solving large-scale eigenvalue problems poses a significant challenge due to the computational complexity and limitations on the parallel scalability of the orthogonalization opera…