Hierarchical Matrices: Algorithms and Analysis

By Wolfgang Hackbusch. 2015

420 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Numerical Analysis, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/55789ecfaf081dcd3105b47a563c3b187740fb4d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Alexander Litvinenko
2. David E. Keyes
3. Marc G. Genton
4. Ying Sun
5. W. Abbett
6. Randall J. LeVeque

In Numerical Analysis

1. Alexander Litvinenko
2. David E. Keyes
3. Marc G. Genton
4. Ying Sun
5. W. Abbett
6. Randall J. LeVeque

In Statistical and Nonlinear Physics

1. Alexander Litvinenko
2. David E. Keyes
3. Marc G. Genton
4. Ying Sun

Citations in the corpus

• Likelihood approximation with hierarchical matrices for large spatial datasets (2017, methodology): “Previous results (Hackbusch, 2015) show that the H-matrix technique is very stable when approximating the matrix itself (Li et al., 2014; Saibaba et al., 2012; Ballani and Kressner, 2015; Harbrecht et al., 2015; Ambikasaran et al., 2013; Börm and Garcke, 2007; Saibaba and Kitanidis, 2012), its…”