Skip to main content
Log in

Differentiating matrix orthogonal transformations

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

Abstract

New numerical algorithms for differentiating matrix orthogonal transformations are constructed. They do not require that the derivatives of the orthogonal transformation matrix be available. An example is given how these algorithms can be applied to the numerically stable calculation of a solution to the discrete-time matrix Riccati sensitivity equation.

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. V. V. Voevodin, Computational Foundations of Linear Algebra (Nauka, Moscow, 1977) [in Russian].

    Google Scholar 

  2. A. A. Samarskii and A. V. Gulin, Numerical Methods (Nauka, Moscow, 1989) [in Russian].

    MATH  Google Scholar 

  3. J. R. Rice, Matrix Computations and Mathematical Software (McGraw-Hill, New York, 1981; Mir, Moscow, 1984).

    MATH  Google Scholar 

  4. I. V. Semushin, Numerical Methods in Algebra (Ul’yanov. Gos. Tekh. Univ., Ul’yanovsk, 2006) [in Russian].

    Google Scholar 

  5. G. H. Golub and C. F. van Loan, Matrix Computations (Johns Hopkins Univ. Press, Baltimore, Md., 1996; Mir, Moscow, 1999).

    MATH  Google Scholar 

  6. T. Kailath, A. H. Sayed, and B. Hassibi, Linear Estimation (Prentice Hall, Englewood Cliffs, N.J., 2000).

    Google Scholar 

  7. G. J. Bierman, Factorization Methods for Discrete Sequential Estimation (Academic, New York, 1977).

    MATH  Google Scholar 

  8. M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 2nd ed. (Wiley, London, 2001).

    Google Scholar 

  9. M. Veriaegen and P. Van Dooren, “Numerical aspects of different Kalman filter implementations,” IEEE Trans. Autom. Control 31 (10), 907–917 (1986).

    Article  Google Scholar 

  10. P. C. Kaminski, A. E. Bryson, and S. F. Schmidt, “Discrete square root filtering: A survey of current techniques,” IEEE Trans. Autom. Control 16 (6), 727–735 (1971).

    Article  Google Scholar 

  11. I. V. Semushin, Yu. V. Tsyganova, M. V. Kulikova, O. A. Fat’yanova, and A. E. Kondrat’ev Adaptive Systems for Filtering, Control, and Fault Detection (Ul’yanov. Gos. Tekh. Univ., Ul’yanovsk, 2011) [in Russian].

    Google Scholar 

  12. N. K. Gupta and R. K. Mehra, “Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations,” IEEE Trans. Autom. Control 19 (6), 774–783 (1974).

    Article  MATH  MathSciNet  Google Scholar 

  13. K. J. Aström, “Maximum likelihood and prediction error methods,” Automatica 16, 551–574 (1980).

    Article  MATH  Google Scholar 

  14. G. J. Bierman, M. R. Belzer, J. S. Vapdergraft, and D. W. Porter, “Maximum likelihood estimation using square root information filters,” IEEE Trans. Autom. Control 35 (12), 1293–1299 (1990).

    Article  MATH  Google Scholar 

  15. M. V. Kulikova, “Likelihood gradient evaluation using square-root covariance filters,” IEEE Trans. Autom. Control 54 (3), 646–651 (2009).

    Article  MathSciNet  Google Scholar 

  16. M. V. Kulikova, “Maximum likelihood estimation via the extended covariance and combined square-root filters,” Math. Comput. Simul. 79, 1641–1657 (2009).

    Article  MATH  MathSciNet  Google Scholar 

  17. M. V. Kulikova, “Maximum likelihood estimation of linear stochastic systems in the class of sequential squareroot orthogonal filtering methods,” Autom. Remote Control 72 (4), 766–786 (2011).

    Article  MATH  MathSciNet  Google Scholar 

  18. I. V. Semushin and Yu. V. Tsyganova, “Adaptive square-root covariance algorithm for filtering in navigation systems,” Proceedings of International Conference on Promising Information Technologies in Aviation and Space (PIT-2010) (Samara, 2010), pp. 118–122.

    Google Scholar 

  19. Yu. V. Tsyganova, “Computing the gradient of the auxiliary quality functional in the parametric identification problem for stochastic systems,” Autom. Remote Control 72 (9), 1925–1940 (2011).

    Article  MATH  MathSciNet  Google Scholar 

  20. Yu. V. Tsyganova and M. V. Kulikova, “On efficient parametric identification methods for linear discrete stochastic systems,” Autom. Remote Control 73 (6), 962–975 (2012).

    Article  MATH  MathSciNet  Google Scholar 

  21. L. Died and T. Eirola, “On smooth decompositions of matrices,” SIAM. J. Matrix Anal. Appl. 20 (3), 800–819 (1999).

    Article  MathSciNet  Google Scholar 

  22. M. A. Ogarkov, Methods for Statistical Parameter Estimation in Stochastic Processes (Energoatomizdat, Moscow, 1990) [in Russian].

    Google Scholar 

  23. P. Park and T. Kailath, “New square-root algorithms for Kalman filtering,” IEEE Trans. Autom. Control 40 (5), 895–899 (1995).

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. V. Kulikova.

Additional information

Original Russian Text © M.V. Kulikova, Yu.V. Tsyganova, 2015, published in Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, 2015, Vol. 55, No. 9, pp. 1460–1473.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kulikova, M.V., Tsyganova, Y.V. Differentiating matrix orthogonal transformations. Comput. Math. and Math. Phys. 55, 1419–1431 (2015). https://doi.org/10.1134/S0965542515090109

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0965542515090109

Keywords

Navigation