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An efficient and reliable quadrature algorithm for integration with respect to binomial measures

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Abstract

In this work numerical methods for integration with respect to binomial measures are considered. Binomial measures are examples of fractal measures and arise when multifractal properties are investigated. Interpolatory quadrature rules are considered. An automatic integrator with local quadrature rules that generalize the five points Newton Cotes formula and error estimates based on null rules is then described. Numerical tests are performed to verify the efficiency and accuracy of the method. These tests confirm that the automatic integrator turns out to be as good as one of the best known quadrature algorithms with respect to the Lebesgue measure.

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References

  1. P. Abry, R. Baraniuk, P. Flandrin, R. Riedi, and D. Veitch, The multiscale nature of network traffic: Discovery, analysis, and modelling, IEEE Signal Process. Magazine, 19(3) (2002), pp. 28–46.

    Article  Google Scholar 

  2. L. Ambrosio, N. Fusco, and D. Pallara, Functions of Bounded Variation and Free Discontinuity Problems, The Clarendon Press/Oxford University Press, New York, 2000, (Oxford Mathematical Monographs).

    MATH  Google Scholar 

  3. A. Barinka, T. Barsch, S. Dahlke, M. Mommer, and M. Konik, Quadrature formulas for refinable functions and wavelets. II. Error analysis, J. Comput. Anal. Appl., 4(4) (2002), pp. 339–361.

    MATH  MathSciNet  Google Scholar 

  4. M. F. Barnsley, Fractals Everywhere, 2nd edn., Academic Press Professional, Boston, MA, 1993, (Revised with the assistance of and with a foreword by H. Rising, III).

    MATH  Google Scholar 

  5. M. F. Barnsley and S. Demko, Iterated function systems and the global construction of fractals, Proc. R. Soc. Lond., Ser. A, 399(1817) (1985), pp. 243–275.

    Article  MATH  MathSciNet  Google Scholar 

  6. J. Berntsen and T. O. Espelid, Error estimation in automatic quadrature routines, ACM Trans. Math. Softw., 17(2) (1991), pp. 233–253.

    Article  MATH  MathSciNet  Google Scholar 

  7. F. Calabrò and A. Corbo Esposito, Binomial measures and quadrature formulae, submitted, preprint avaible on arXiv.org: math.NA/0612190, 2008.

  8. L. Carbone, G. Cardone, and A. Corbo Esposito, Binary digits expansion of numbers: Hausdorff dimensions of intersections of level sets of avarages’ upper and lower limits, Sci. Math. Jpn., 60(2) (2004), pp. 347–356.

    MATH  MathSciNet  Google Scholar 

  9. G. Cardone, A. Corbo Esposito, and L. Faella, Hausdorff dimension for level sets of upper and lower limits of generalized averages of binary digits, Math. Methods Appl. Sci., 29(16) (2006), pp. 1983–2008.

    Article  MATH  MathSciNet  Google Scholar 

  10. T. O. Espelid, Doubly Adaptive Quadrature Routines Based on Newton–Cotes Rules, Reports in Informatics, vol. 229, Dept. of Informatics, Univ. of Bergen, Norway, 2002, pp. 1–34.

    Google Scholar 

  11. T. O. Espelid, Doubly adaptive quadrature routines based on Newton–Cotes rules, BIT, 43 (2003), pp. 319–337.

    Article  MATH  MathSciNet  Google Scholar 

  12. T. O. Espelid, Algorithm 868: Globally doubly adaptive quadrature – Reliable Matlab codes, ACM Trans. Math. Softw., 33(3) (2007), pp. E1–E21.

    Article  MathSciNet  Google Scholar 

  13. W. Gander and W. Gautschi, Adaptive quadrature – revisited, BIT, 40(1) (2000), pp. 84–101.

    Article  MathSciNet  Google Scholar 

  14. W. Gautschi, A survey of Gauss–Christoffel quadrature formulae, in E.B. Christoffel, P. L. Butzer and F. Fehér, eds., Birkhäuser, Basel, 1981, pp. 72–147.

    Google Scholar 

  15. W. Gautschi, Orthogonal polynomials and quadrature, Electron. Trans. Numer. Anal., 9 (1999), pp. 65–76.

    MATH  MathSciNet  Google Scholar 

  16. W. Gautschi, L. Gori, and F. Pitolli, Gauss quadrature for refinable weight functions, Appl. Comput. Harmon. Anal., 8(3) (2000), pp. 249–257.

    Article  MATH  MathSciNet  Google Scholar 

  17. J. E. Hutchinson, Fractals and self-similarity, Indiana Univ. Math. J., 30(5) (1981), pp. 713–747.

    Article  MATH  MathSciNet  Google Scholar 

  18. D. Huybrechs and S. Vandewalle, Composite quadrature formulae for the approximation of wavelet coefficients of piecewise smooth and singular functions, J. Comput. Appl. Math., 180(1) (2005), pp. 119–135.

    Article  MATH  MathSciNet  Google Scholar 

  19. D. P. Laurie and J. M. de Villiers, Orthogonal polynomials for refinable linear functionals, Math. Comput., 75(256) (2006), pp. 1891–1903 (electronic).

    Article  MATH  Google Scholar 

  20. H. Liangxiu, C. Zhiwei, C. Chunbo, and G. Chuanshan, A new multifractal network traffic model, Chaos Solitons Fractals, 12 (2002), pp. 1507–1513.

    Article  MathSciNet  Google Scholar 

  21. G. Mantica, A stable Stieltjes technique for computing orthogonal polynomials and Jacobi matrices associated with a class of singular measures, Constructive Approximation, 12(4) (1996), pp. 509–530.

    MATH  MathSciNet  Google Scholar 

  22. G. Mantica, Fractal measures and polynomial sampling: IFS-Gaussian integration, Numer. Algorithms, 45(1–4) (2007), pp. 269–281.

    Article  MATH  MathSciNet  Google Scholar 

  23. G. Mantica and D. Guzzetti, The asymptotic behaviour of the Fourier transforms of orthogonal polynomials. II. L.I.F.S. measures and quantum mechanics, Ann. Henri Poincaré, 8(2) (2007), pp. 301–336.

    Article  MATH  MathSciNet  Google Scholar 

  24. L. Olsen, Multifractal geometry, Prog. Probab., 46 (2000), pp. 3–37.

    MathSciNet  Google Scholar 

  25. H.-O. Peitgen, H. Jürgens, and D. Saupe, Chaos and Fractals: New Frontiers of Science, Springer, New York, 1992 (With a foreword by M. J. Feigenbaum, Appendix A by Y. Fisher, Appendix B by C. J. G. Evertsz and B. B. Mandelbrot).

    Google Scholar 

  26. Y. Pesin and H. Weiss, The multifractal analysis of Gibbs measures: Motivation, mathematical foundation, and examples, Chaos, 7(1) (1997), pp. 89–106.

    Article  MATH  MathSciNet  Google Scholar 

  27. Y. Pesin and H. Weiss, The maltifractal analysis of Birkhoff averages and large deviations, in Global Analysis of Dynamical Systems, IoP Publishing, Bristol, 2001.

    Google Scholar 

  28. A. Quarteroni, R. Sacco, and F. Saleri, Numerical Mathematics, 2nd edn., Texts Appl. Math., vol. 37, Springer, Berlin, 2007.

    MATH  Google Scholar 

  29. R. H. Riedi, Multifractal processes, in Theory and Applications of Longrange Dependence, Birkhäuser, Boston, MA, 2003, pp. 625–716.

    Google Scholar 

  30. W. Rudin, Real and Complex Analysis, 2nd edn., McGraw-Hill, New York, 1974 (McGraw-Hill Series in Higher Mathematics).

    MATH  Google Scholar 

  31. A. Venter and D. P. Laurie, A selection criterion in a doubly adaptive integration algorithm, BIT, 42(1) (2002), pp. 183–193.

    Article  MATH  MathSciNet  Google Scholar 

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Correspondence to F. Calabrò.

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AMS subject classification (2000)

28A25, 60G18, 65D30, 65D32, 68M15

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Calabrò, F., Corbo Esposito, A. An efficient and reliable quadrature algorithm for integration with respect to binomial measures . Bit Numer Math 48, 473–491 (2008). https://doi.org/10.1007/s10543-008-0168-x

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  • DOI: https://doi.org/10.1007/s10543-008-0168-x

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