The LR Cholesky algorithm for symmetric hierarchical matrices

Peter Benner, Thomas Mach

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


We investigate the application of the LR Cholesky algorithm to symmetric hierarchical matrices, symmetric simple structured hierarchical matrices and symmetric hierarchically semiseparable (HSS) matrices. The data-sparsity of these matrices make the otherwise expensive LR Cholesky algorithm applicable, as long as the data-sparsity is preserved. We will see in an example that the ranks of the low rank blocks grow and the data-sparsity gets lost. We will explain this behavior by applying a theorem on the structure preservation of diagonal plus semiseparable matrices under LR Cholesky transformations. Therefore we have to give a new more constructive proof for the theorem. We will show that the structure of Hℓ-matrices is almost preserved and so the LR Cholesky algorithm is of almost quadratic complexity for Hℓ-matrices.

Original languageEnglish
Pages (from-to)1150-1166
Number of pages17
JournalLinear Algebra and Its Applications
Issue number4
Publication statusPublished - 2013
Externally publishedYes


  • Eigenvalues
  • Hℓ-Matrices
  • LR Cholesky algorithm
  • Semiseparable matrices
  • Symmetric hierarchical matrices

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Numerical Analysis
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics

Fingerprint Dive into the research topics of 'The LR Cholesky algorithm for symmetric hierarchical matrices'. Together they form a unique fingerprint.

Cite this