Skip to main content
Log in

Minimization methods for approximating tensors and their comparison

  • Published:
Computational Mathematics and Mathematical Physics Aims and scope Submit manuscript

Abstract

Application of various minimization methods to trilinear approximation of tensors is considered. These methods are compared based on numerical calculations. For the Gauss-Newton method, an efficient implementation is proposed, and the local rate of convergence is estimated for the case of completely symmetric tensors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R. A. Harshman, “Foundations of the Parafac Procedure: Models and Conditions for an Explanatory Multimodal Factor Analysis,” UCLA Working Papers in Phonetics 16, 1–84 (1970).

    Google Scholar 

  2. P. Comon, “Tensor Decomposition: State of the Art and Applications,” in IMA Conf. Math. Signal Proc., Warwick, UK, 2000; http://www.i3s.fr/:_comon/FichiersPs/ima2000.ps.

  3. I. Ibraghimov, “Application of the Three-Way Decomposition for Matrix Compression,” Numer. Lin. Algebra Appl. 9, 551–565 (2002).

    Article  MATH  MathSciNet  Google Scholar 

  4. J. D. Caroll and J. J. Chang, “Analysis of Individual Differences in Multidimensional Scaling via N-Way Generalization of Eckart-Young Decomposition,” Psychometrica 35, 283–319 (1970).

    Article  Google Scholar 

  5. R. Bro, “PARAFAC: Tutorial and Applications,” Chemom. Intel. Lab. Systems 38, 149–171 (1997).

    Article  Google Scholar 

  6. J.-H. Wang, P. K. Hopke, T. M. Hancewicz, and S. L. Zhang, “Application of Modified Least Squares Regression to Spectroscopic Image Analysis,” Analys. Chim. Acta 476, 93–109 (2003).

    Article  Google Scholar 

  7. J. P. Dedieu and M. Schub, “Newton’s Method for Overdetermined Systems of Equations,” Math. Comput. 69(281), 1099–1115 (2000).

    MATH  Google Scholar 

  8. T. Zhang and G. H. Golub, “Rank-One Approximation to High-Order Tensors,” SIAM J. Matrix Anal. Appl. 23, 534–550 (2001).

    Article  MATH  MathSciNet  Google Scholar 

  9. H. B. Nielsen, “Damping Parameter in Marquardt’s Method,” Inform. Math. Model., Denmark (1999); http://www/imm.dtu.dk/:_hbn/publ/TR9905.ps.

  10. L. R. Tucker, “Some Mathematical Notes on Three-Mode Factor Analysis,” Psychometrica 31, 279–311 (1966).

    Article  MathSciNet  Google Scholar 

  11. I. V. Oseledets and D. V. Savost’yanov, “A Fast Algorithm for Simultaneous Reduction of Matrixes to a Triangular Form and Approximation of Tensors,” in Matrix Methods for Solving Large-Scale Problems (Institut Vychislitel’noi Matematiki, RAN, Moscow, 2005), pp. 101–116 [in Russian].

    Google Scholar 

  12. I. V. Oseledets and D. V. Savost’yanov, “Methods for Decomposition of Tensors,” in Matrix Methods for Solving Large-Scale Problems (Institut Vychislitel’noi Matematiki, RAN, Moscow, 2005), pp. 51–64 [in Russian].

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Original Russian Text © I.V. Oseledets, D.V. Savost’yanov, 2006, published in Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 2006, Vol. 46, No. 10, pp. 1725–1734.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Oseledets, I.V., Savost’yanov, D.V. Minimization methods for approximating tensors and their comparison. Comput. Math. and Math. Phys. 46, 1641–1650 (2006). https://doi.org/10.1134/S0965542506100022

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0965542506100022

Keywords

Navigation