MATLAB tensor classes for fast algorithm prototyping.

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Tensors (also known as mutidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to psychometrics. We describe four MATLAB classes for tensor manipulations that can be used for fast algorithm prototyping. The tensor class extends the functionality of MATLAB's multidimensional arrays by supporting additional operations such as tensor multiplication. The tensor as matrix class supports the 'matricization' of a tensor, i.e., the conversion of a tensor to a matrix (and vice versa), a commonly used operation in many algorithms. Two additional classes represent tensors stored in decomposed formats: cp tensor and tucker tensor. We ... continued below

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52 p.

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Bader, Brett William & Kolda, Tamara Gibson (Sandia National Laboratories, Livermore, CA) October 1, 2004.

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Description

Tensors (also known as mutidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to psychometrics. We describe four MATLAB classes for tensor manipulations that can be used for fast algorithm prototyping. The tensor class extends the functionality of MATLAB's multidimensional arrays by supporting additional operations such as tensor multiplication. The tensor as matrix class supports the 'matricization' of a tensor, i.e., the conversion of a tensor to a matrix (and vice versa), a commonly used operation in many algorithms. Two additional classes represent tensors stored in decomposed formats: cp tensor and tucker tensor. We descibe all of these classes and then demonstrate their use by showing how to implement several tensor algorithms that have appeared in the literature.

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52 p.

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  • Report No.: SAND2004-5187
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/974890 | External Link
  • Office of Scientific & Technical Information Report Number: 974890
  • Archival Resource Key: ark:/67531/metadc927897

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Creation Date

  • October 1, 2004

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 29, 2016, 7:31 p.m.

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Bader, Brett William & Kolda, Tamara Gibson (Sandia National Laboratories, Livermore, CA). MATLAB tensor classes for fast algorithm prototyping., report, October 1, 2004; United States. (digital.library.unt.edu/ark:/67531/metadc927897/: accessed May 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.