HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification

Alexander Litvinenko, Ronald Kriemann, Marc G. Genton, Ying Sun, David E. Keyes

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: • approximates large dense inhomogeneous covariance matrices with a log-linear computational cost and storage requirement; •computes matrix-vector product, Cholesky factorization and inverse with a log-linear complexity; •identify unknown parameters of the covariance function (variance, smoothness, and covariance length); These unknown parameters are estimated by maximizing the joint Gaussian log-likelihood function. To demonstrate the numerical performance, we identify three unknown parameters in an example with 2,000,000 locations on a PC-desktop.
Original languageEnglish (US)
Pages (from-to)100600
JournalMethodsX
Volume7
DOIs
StatePublished - Jul 12 2019

Fingerprint

Dive into the research topics of 'HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification'. Together they form a unique fingerprint.

Cite this