Data sparse computation of the Karhunen-Loève expansion

B. N. Khoromskij, A. Litvinenko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


Realistic mathematical models of physical processes contain uncertainties. These models are often described by stochastic differential equations (SDEs) or stochastic partial differential equations (SPDEs) with multiplicative noise, where uncertainties in, e.g. the right-hand side or the coefficients are represented as random fields. To solve a given SPDE numerically one has to discretise the deterministic operator as well as the stochastic fields. The total dimension of the SPDE is the product of the dimensions of the deterministic part and the stochastic part. For approximation of random fields with as few random variables as possible, but still retaining the essential information, the Karhunen-Loève expansion (KLE) becomes important. The KLE of a random field requires the solution of a large eigenvalue problem. Usually it is solved by a Krylov subspace method with a sparse matrix approximation. We demonstrate the use of the low-rank and data sparse hierarchical matrix technique for solving this problem. A log-linear computational cost of the matrix-vector product and a log-linear storage requirement yield to the efficient and fast discretisation of the present random fields.

Original languageEnglish (US)
Title of host publicationNumerical Analysis and Applied Mathematics - International Conference on Numerical Analysis and Applied Mathematics 2008
Number of pages4
StatePublished - 2008
Externally publishedYes
EventInternational Conference on Numerical Analysis and Applied Mathematics, ICNAAM 2008 - Psalidi, Kos, Greece
Duration: Sep 16 2008Sep 20 2008

Publication series

NameAIP Conference Proceedings
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616


OtherInternational Conference on Numerical Analysis and Applied Mathematics, ICNAAM 2008
CityPsalidi, Kos


  • Hierarchical matrices
  • Karhunen-Loève expansion
  • Kronecker tensor format
  • Random fields
  • SFEM
  • Stochastic Galerkin

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'Data sparse computation of the Karhunen-Loève expansion'. Together they form a unique fingerprint.

Cite this