Low-rank eigenvalue solvers for block-sparse matrix product states

We consider an iterative eigensolver for Schrödinger equations that constructs low-rank approximations of eigenfunctions with accuracy-adapted ranks, with particular focus on fermionic Schrödinger equations in second-quantized form and on matrix product state approximations enforcing particle number conservation. We provide a complete analysis of a solver based on preconditioned inverse iteration combined with rank truncation and propose a generalization to subspace iteration for the joint approximation of several eigenspaces. The practical performance of the method is illustrated by numerical tests for several model problems.

Source: arXiv:2604.16118v1 - http://arxiv.org/abs/2604.16118v1 PDF: https://arxiv.org/pdf/2604.16118v1 Original Link: http://arxiv.org/abs/2604.16118v1