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Journal of Scientific Computing

, Volume 70, Issue 2, pp 478–499 | Cite as

Subspace Methods with Local Refinements for Eigenvalue Computation Using Low-Rank Tensor-Train Format

  • Junyu Zhang
  • Zaiwen Wen
  • Yin Zhang
Article
  • 242 Downloads

Abstract

Computing a few eigenpairs from large-scale symmetric eigenvalue problems is far beyond the tractability of classic eigensolvers when the storage of the eigenvectors in the classical way is impossible. We consider a tractable case in which both the coefficient matrix and its eigenvectors can be represented in the low-rank tensor train formats. We propose a subspace optimization method combined with some suitable truncation steps to the given low-rank Tensor Train formats. Its performance can be further improved if the alternating minimization method is used to refine the intermediate solutions locally. Preliminary numerical experiments show that our algorithm is competitive to the state-of-the-art methods on problems arising from the discretization of the stationary Schrödinger equation.

Keywords

High-dimensional eigenvalue problem Tensor-train format Alternating least square method Subspace optimization method 

Notes

Acknowledgments

We thank D. Kressner, M. Steinlechner and A. Uschmajew for sharing online their matlab codes on EVAMEn and the TT/MPS tensor toolbox TTeMPS. The authors would like to thank the associate editor Prof. Wotao Yin and two anonymous referees for their detailed and valuable comments and suggestions.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
  3. 3.Department of Computational and Applied MathematicsRice UniversityHoustonUSA

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