hapod - Hierarchical Approximate Proper Orthogonal Decomposition. The HAPOD is an algorithm to compute the POD (left singular vectors, and singular values of a matrix) hierarchically for (column-wise partitioned) large-scale matrices, allowing to balance accuracy with performance. As a POD-of-PODs method, the HAPOD can be parallelized and further accelerated by user supplied SVD implementations.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Leibner, Tobias; Ohlberger, Mario: A new entropy-variable-based discretization method for minimum entropy moment approximations of linear kinetic equations (2021)
  2. Fareed, Hiba; Singler, John R.: Error analysis of an incremental proper orthogonal decomposition algorithm for PDE simulation data (2020)
  3. Balabanov, Oleg; Nouy, Anthony: Randomized linear algebra for model reduction. I. Galerkin methods and error estimation (2019)
  4. Benner, Peter; Himpe, Christian: Cross-Gramian-based dominant subspaces (2019)
  5. Fareed, Hiba; Singler, John R.: A note on incremental POD algorithms for continuous time data (2019)
  6. Lehrenfeld, Christoph; Rave, Stephan: Mass conservative reduced order modeling of a free boundary osmotic cell swelling problem (2019)
  7. Taddei, Tommaso: An offline/online procedure for dual norm calculations of parameterized functionals: empirical quadrature and empirical test spaces (2019)
  8. Fareed, Hiba; Singler, John R.; Zhang, Yangwen; Shen, Jiguang: Incremental proper orthogonal decomposition for PDE simulation data (2018)
  9. Fick, Lambert; Maday, Yvon; Patera, Anthony T.; Taddei, Tommaso: A stabilized POD model for turbulent flows over a range of Reynolds numbers: optimal parameter sampling and constrained projection (2018)
  10. Himpe, Christian: \textttemgr-- the empirical Gramian framework (2018)
  11. Himpe, Christian; Leibner, Tobias; Rave, Stephan: Hierarchical approximate proper orthogonal decomposition (2018)
  12. Taddei, Tommaso; Patera, Anthony T.: A localization strategy for data assimilation; application to state estimation and parameter estimation (2018)