High-dimensional extreme eigenvalue problems: low-rank tensor parametrization and optimization
Abstract
High-dimensional extreme eigenvalue problems often arise from molecular vibrational models, electronic structure calculations, and quantum mechanics. Directly solving these problems suffers from the curse of dimensionality. Instead of tackling the problem in high-dimensional ambient space, we reformulate the problem through low-rank tensor formats, which can significantly reduce the computational cost and storage. Specifically, we consider the Rayleigh--Ritz problem on bounded-rank tensors in the tensor train format, which enables a more flexible choice of rank parameters. Moreover, we consider a smooth parametrization for bounded-rank tensors by introducing slack variables, leading to a smooth manifold structure. The original Rayleigh--Ritz problem is therefore transferred to an optimization problem on the manifold. We develop the Riemannian geometry and propose optimization methods for solving extreme eigenvalue problems. In practice, the Kronecker-product structure in Hamiltonian and PDE operators is employed to simplify the computation. Numerical experiments on the harmonic oscillator, the Laplace operator, the Schrödinger equation, the layered cluster problem, and the Bose--Einstein model demonstrate that the proposed method achieves accuracy comparable to full-space eigensolvers with reduced computation time and avoids storing full vectors. The results also show numerical rank reduction during iteration and robustness when the rank parameter is over-estimated. In addition, the fixed-point iterations illustrate that the proposed methods are able to serve as an inner eigensolver for solving nonlinear eigenvalue problems.
Related
- Interactive Torus Parametrization of the One-Dimensional Fibonacci Quasicrystal
- Edgeworth expansions of extreme eigenvalue distributions for random unitary ensembles
- Advancements in Spectral Collocation Methods for High-Order Eigenvalue Problems
- Conditioning and backward errors for nonlinear eigenvalue problems with eigenvector nonlinearities
- Homotopy for Rectangular Multiparameter Eigenvalue Problems
BibTeX
@misc{hao2026highdimensional,
title = {{High-dimensional extreme eigenvalue problems: low-rank tensor parametrization and optimization}},
author = {Pengfei Hao and Renfeng Peng and Yang Kuang and Bin Gao},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30138},
eprint = {2609.30138},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30138},
}
RIS
TY - UNPB
TI - High-dimensional extreme eigenvalue problems: low-rank tensor parametrization and optimization
AU - Pengfei Hao
AU - Renfeng Peng
AU - Yang Kuang
AU - Bin Gao
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.30138
UR - https://arxiv.org/abs/2609.30138
AB - High-dimensional extreme eigenvalue problems often arise from molecular vibrational models, electronic structure calculations, and quantum mechanics. Directly solving these problems suffers from the curse of dimensionality. Instead of tackling the problem in high-dimensional ambient space, we reformulate the problem through low-rank tensor formats, which can significantly reduce the computational cost and storage. Specifically, we consider the Rayleigh--Ritz problem on bounded-rank tensors in the tensor train format, which enables a more flexible choice of rank parameters. Moreover, we consider a smooth parametrization for bounded-rank tensors by introducing slack variables, leading to a smooth manifold structure. The original Rayleigh--Ritz problem is therefore transferred to an optimization problem on the manifold. We develop the Riemannian geometry and propose optimization methods for solving extreme eigenvalue problems. In practice, the Kronecker-product structure in Hamiltonian and PDE operators is employed to simplify the computation. Numerical experiments on the harmonic oscillator, the Laplace operator, the Schrödinger equation, the layered cluster problem, and the Bose--Einstein model demonstrate that the proposed method achieves accuracy comparable to full-space eigensolvers with reduced computation time and avoids storing full vectors. The results also show numerical rank reduction during iteration and robustness when the rank parameter is over-estimated. In addition, the fixed-point iterations illustrate that the proposed methods are able to serve as an inner eigensolver for solving nonlinear eigenvalue problems.
ER -