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Algorithm 929: A suite on wavelet differentiation algorithms

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Published:23 July 2013Publication History
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A collection of the Matlab routines that compute the values of the scaling and wavelet functions (φ(x) and ψ(x) respectively) and the derivative of an arbitrary function (periodic or non periodic) using wavelet bases is presented. Initially, the case of Daubechies wavelets is taken and the procedure is explained for both collocation and Galerkin approaches. For each case a Matlab routine is provided to compute the differentiation matrix and the derivative of the function f(d) = D(d)f. Moreover, the convergence of the derivative is shown graphically as a function of different parameters (the wavelet genus, D and the scale, J) for two test functions. We then consider the use of spline wavelets.

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References

  1. Cai, W. and Wang, J. 1996. Adaptive multiresolution collocation methods for initial boundary value problems of nonlinear PDEs. SIAM J. Numer. Anal. 33, 937--970. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Canuto, C., Hussaini, M. Y., Quarteroni, A., and Zang, T. A. 1988. Spectral Methods in Fluid Dynamics. Springer series in computational physics. Springer.Google ScholarGoogle Scholar
  3. Cohen, A., Daubechies, I., and Vial, P. 1993. Wavelets on the interval and fast wavelet transform. Appl. Comput. Harmonic Anal. 1, 54--81.Google ScholarGoogle ScholarCross RefCross Ref
  4. Dahmen, W. 1997. Wavelet and multiscale methods for operator equations. Acta Numerica 6, 55--228.Google ScholarGoogle ScholarCross RefCross Ref
  5. Dahmen, W., Urban, K., and Vorloeper, J. 2002. Adaptive wavelet methods: basic concepts and applications to the Stokes problem. In Wavelet Analysis: Twenty Years' Developments, D.-X. Zhou, Ed. Series in Analysis, vol. 1. World Scientific Publishing, Singapore, 39--80.Google ScholarGoogle Scholar
  6. Daubechies, I. 1992. Ten Lectures on Wavelets. Number 61 in CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM, Philadelphia, PA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Garba, A. 1996. A wavelet collocation method for numerical solution of Burgers' equation. Tech. rep., International Center for Theorytical Physics.Google ScholarGoogle Scholar
  8. Gerald, C. and Wheatley, P. 2004. Applied Numerical Analysis, 7th ed. Pearson Education, Inc.Google ScholarGoogle Scholar
  9. Jameson, L. 1993. On the Daubechies-based wavelet differentiation matrix. J. Sci. Comput. 8, 3, 267--305. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Jameson, L. 1995. On the spline based wavelet differentiation matrix. Appl. Numer. Math. 17, 33--45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jameson, L. 1996. The differentiation matrix for Daubechies based wavelets on an interval. SIAM J. Sci. Comput. 17, 498--516. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kumar, V. and Mehra, M. 2007. Cubic spline adaptive wavelet scheme to solve singularly perturbed reaction diffusion problems. Int. J. Wavelets Multiresolution Inf. Process. 5, 317--331.Google ScholarGoogle ScholarCross RefCross Ref
  13. Mallat, S. G. 1989. Multiresolution approximations and wavelet orthonormal bases of L2(R). Trans. Amer. Math. Soc. 315, 69--87.Google ScholarGoogle Scholar
  14. Mehra, M. and Kevlahan, N. K.-R. 2008. An adaptive wavelet collocation method for the solution of partial differential equations on the sphere. J. Comput. Phys. 227, 5610--5632. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Mehra, M. and Kumar, B. V. R. 2005. Time-accurate solution of advection-diffusion problems by wavelet-Taylor-Galerkin method. Commun. Numer. Methods Eng. 21, 313--326.Google ScholarGoogle ScholarCross RefCross Ref
  16. Nielsen, O. M. 1998. Wavelets in scientific computing. Ph.D. thesis, Technical University of Denmark, Lyngby.Google ScholarGoogle Scholar
  17. Schneider, K. and Fröhlich, J. 1997. An adaptive wavelet-vaguelette algorithm for the solution of PDEs. J. Comput. Phys. 130, 90--174. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Strang, G. and Nguyen, T. 1996. Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, MA.Google ScholarGoogle Scholar
  19. Unser, M. and Aldroubi, A. 1992. Polynomial splines and wavelets: A signal processing perspective. In Wavelets: A Tutorial in Theory and Applications, C. K. Chui, Ed. Academic Press Professional, Inc., San Diego, CA, 91--122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Urban, K. 2009. Wavelet Methods for Elliptic Partial Differential Equations. Oxford University Press.Google ScholarGoogle Scholar
  21. Vasilyev, O. and Kevlahan, N.-R. 2005. An adaptive multilevel wavelet collocation method for elliptic problems. J. Comput. Phys. 206, 412--431. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Vasilyev, O. V. and Bowman, C. 2000. Second generation wavelet collocation method for the solution of partial differential equations. J. Comput. Phys. 165, 660--693. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Transactions on Mathematical Software
      ACM Transactions on Mathematical Software  Volume 39, Issue 4
      July 2013
      163 pages
      ISSN:0098-3500
      EISSN:1557-7295
      DOI:10.1145/2491491
      Issue’s Table of Contents

      Copyright © 2013 ACM

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      Publication History

      • Published: 23 July 2013
      • Accepted: 1 October 2012
      • Revised: 1 June 2012
      • Received: 1 October 2011
      Published in toms Volume 39, Issue 4

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