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Title: MATLAB tensor classes for fast algorithm prototyping.

MATLAB tensor classes for fast algorithm prototyping. 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.
Authors: ;
Publication Date:
OSTI Identifier:974890
Report Number(s):SAND2004-5187
TRN: US201008%%425
DOE Contract Number:AC04-94AL85000
Resource Type:Technical Report
Data Type:
Research Org:Sandia National Laboratories
Sponsoring Org:USDOE
Country of Publication:United States
Language:English
Subject: 97 MATHEMATICAL METHODS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; COMPUTER CODES; TENSORS Image processing.; Image analysis.; Arrays.; Calculus of tensors.