Proceedings of International Conference on Applied Innovation in IT  ·  2017/03/16  ·  Vol. 5  ·  Issue 1  ·  pp. 109–112
Projection method for solving systems of linear equations using wavelet packet decomposition of the residual
Vasily Esaulov, Roman Sinetsky
The work is devoted to the problem of solving large systems of linear algebraic equations with irregular structure matrices. To solve them the variant of the projection method in the Petrov-Galerkin form is proposed. Most of the known projection methods is based on the use of bases of Krylov subspaces. The main difference of the proposed method is the choice of the basis from coefficients of wavelet packet decomposition of the residuals. In general, the wavelet transform can be adaptive due to the entropic criteria for the evaluation of elements of the wavelet tree. This distinguishes the proposed method from the known FOM method, the GMRES algorithm and other projection solvers. Conducted a series of computational experiments comparing the proposed algorithm with the main existing projection methods. The experiments showed that the proposed algorithm is competitive with the major existing projection type methods, and in some cases can exceed them.
Systems of Linear Equations Projection Methods Wavelet Packet Decomposition Entropy Criteria
References
  1. Saad, Y., 1981. Krylov subspace methods for solving large unsymmetric linear systems. Mathematics of
  2. Computation, 37:105-126.
  3. Reddy, J. N., 2006. An introduction to the finite element method (3rd ed.), Mcgraw–Hill.
  4. Esaulov, V, 2015. An iterative method for solving a system of linear equations using wavelet filters. Engineering journal of Don, No. 4, 2015. http://ivdon.ru/ru/magazine/archive/n4y2015/3372 (In
  5. Russian)
  6. Chui, C. K., 1992. An Introduction to Wavelets. San Diego: Academic Press.
  7. Bracewell, R. N., 2000. The Fourier Transform and Its Applications (3rd ed.), Boston: McGraw-Hill.
  8. Resnikoff, H. L., Wells, R. O. Jr., 1998. Wavelet Analysis. Springer.
  9. Coifman R.R., Wickerhauser, M.V., 1992. Entropy-Based Algorithms for Best Basis Selection. IEEE Transactions on Information Theory, 38(2).
  10. Hansen, P. C., 2007. Regularization Tools Version 4.0 for Matlab 7.3, Numerical Algorithms, 46 (2007):189-194.
  11. Baker, C. T. H., 1977. The Numerical Treatment of Integral Equations, Clarendon Press, Oxford.
  12. Shaw, C. B. Jr., 1972. Improvements of the resolution of an instrument by numerical solution of an integral equation. J. Math. Anal. Appl. 37:83–112.
  13. Baart, M. L., 1982. The use of auto-correlation for pseudorank determination in noisy ill-conditioned linear least-squares problems. IMA J. Numer. Anal., 2:241–247.

Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0  ·  This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

ICAIIT 2026
International Conference on Applied Innovation in IT
Navigation
Publisher
ISSN2199-8876
Location Anhalt University of Applied Sciences
Phone +49 (0) 3496 67 5611
Address Building 01, Room 425
Bernburger Str. 55
D-06366 Köthen, Germany
Open Access License

All works are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise noted.

Published by ICAIIT in cooperation with Anhalt University of Applied Sciences.

© 2026 ICAIIT — International Conference on Applied Innovations in IT. Anhalt University of Applied Sciences, Köthen, Germany.
Visitors: site traffic counter