Algorithm 862

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Algorithm 862: MATLAB tensor classes for fast algorithm prototyping

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ACM Transactions on Mathematical Software (TOMS) archive
Volume 32 , Issue 4 (December 2006) table of contents

Pages: 635 - 653

Year of Publication: 2006

ISSN:0098-3500

Authors

Brett W. Bader	Sandia National Laboratories, Livermore, CA
Tamara G. Kolda	Sandia National Laboratories, Livermore, CA

Publisher

ACM New York, NY, USA

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Software for "MATLAB tensor classes for fast algorithm prototyping"

ABSTRACT

Tensors (also known as multidimensional 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, that is, 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 describe all of these classes and then demonstrate their use by showing how to implement several tensor algorithms that have appeared in the literature.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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