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

Fuente: arXiv
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Auteurs principaux: Bachmayr, Markus, Krämer, Sebastian, Pfeffer, Max
Format: Preprint
Publié: 2026
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author Bachmayr, Markus
Krämer, Sebastian
Pfeffer, Max
author_facet Bachmayr, Markus
Krämer, Sebastian
Pfeffer, Max
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2604_16118
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Low-rank eigenvalue solvers for block-sparse matrix product states
Bachmayr, Markus
Krämer, Sebastian
Pfeffer, Max
Numerical Analysis
15A69, 65F15, 65Y20, 65Z05
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.
title Low-rank eigenvalue solvers for block-sparse matrix product states
topic Numerical Analysis
15A69, 65F15, 65Y20, 65Z05
url https://arxiv.org/abs/2604.16118