Algorithm 941: htucker: htucker - A Matlab toolbox for tensors in hierarchical Tucker format. The hierarchical Tucker format is a storage-efficient scheme to approximate and represent tensors of possibly high order. This paper presents a Matlab toolbox, along with the underlying methodology and algorithms, which provides a convenient way to work with this format. The toolbox not only allows for the efficient storage and manipulation of tensors in hierarchical Tucker format but also others a set of tools for the development of higher-level algorithms. Several examples for the use of the toolbox are given.

References in zbMATH (referenced in 16 articles )

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  1. Corveleyn, Samuel; Vandewalle, Stefan: Computation of the output of a function with fuzzy inputs based on a low-rank tensor approximation (2017)
  2. Hashemi, Behnam; Trefethen, Lloyd N.: Chebfun in three dimensions (2017)
  3. Kressner, Daniel; Periša, Lana: Recompression of Hadamard products of tensors in Tucker format (2017)
  4. Zhang, Junyu; Wen, Zaiwen; Zhang, Yin: Subspace methods with local refinements for eigenvalue computation using low-rank tensor-train format (2017)
  5. Beik, Fatemeh Panjeh Ali; Saberi Movahed, Farid; Ahmadi-Asl, Salman: On the Krylov subspace methods based on tensor format for positive definite Sylvester tensor equations. (2016)
  6. Fan, H.-Y.; Zhang, L.; Chu, E.K.-w.; Wei, Y.: Q-less QR decomposition in inner product spaces (2016)
  7. Andreev, Roman; Tobler, Christine: Multilevel preconditioning and low-rank tensor iteration for space-time simultaneous discretizations of parabolic PDEs. (2015)
  8. Da Silva, Curt; Herrmann, Felix J.: Optimization on the hierarchical Tucker manifold - applications to tensor completion (2015)
  9. Arnold, Andreas; Jahnke, Tobias: On the approximation of high-dimensional differential equations in the hierarchical Tucker format (2014)
  10. Dahlke, Stephan (ed.); Dahmen, Wolfgang (ed.); Griebel, Michael (ed.); Hackbusch, Wolfgang (ed.); Ritter, Klaus (ed.); Schneider, Reinhold (ed.); Schwab, Christoph (ed.); Yserentant, Harry (ed.): Extraction of quantifiable information from complex systems (2014)
  11. Kressner, Daniel; Tobler, Christine: Algorithm 941: htucker -- a Matlab toolbox for tensors in hierarchical Tucker format (2014)
  12. Grasedyck, Lars; Kressner, Daniel; Tobler, Christine: A literature survey of low-rank tensor approximation techniques (2013)
  13. Lubich, Christian; Rohwedder, Thorsten; Schneider, Reinhold; Vandereycken, Bart: Dynamical approximation by hierarchical Tucker and tensor-train tensors (2013)
  14. Uschmajew, André; Vandereycken, Bart: The geometry of algorithms using hierarchical tensors (2013)
  15. Kressner, Daniel; Tobler, Christine: Preconditioned low-rank methods for high-dimensional elliptic PDE eigenvalue problems (2011)
  16. Kressner, Daniel; Tobler, Christine: Low-rank tensor Krylov subspace methods for parametrized linear systems (2011)