Abstract
We compute approximate sparse polynomial models of the form \(\widetilde{f}(x) = \sum _{j=1}^t \widetilde{c}_j (x - \widetilde{s})^{e_j}\) to a function \(f(x)\), of which an approximation of the evaluation \(f(\zeta )\) at any complex argument value \(\zeta \) can be obtained. We assume that several of the returned function evaluations \(f(\zeta )\) are perturbed not just by approximation/noise errors but also by highly perturbed outliers. None of the \(\widetilde{c}_j\), \(\widetilde{s}\), \(e_j\) and the location of the outliers are known beforehand. We use a numerical version of an exact algorithm by [4] together with a numerical version of the Reed–Solomon error correcting coding algorithm. We also compare with a simpler approach based on root finding of derivatives, while restricted to characteristic \(0\). In this preliminary report, we discuss how some of the problems of numerical instability and ill-conditioning in the arising optimization problems can be overcome. By way of experiments, we show that our techniques can recover approximate sparse shifted polynomial models, provided that there are few terms \(t\), few outliers and that the sparse shift is relatively small.
This material is based on work supported in part by the National Science Foundation under Grants CCF-0830347 and CCF-1115772.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ben-Or, M., Tiwari, P.: A deterministic algorithm for sparse multivariate polynomial interpolation. In: Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing, pp. 301–309. ACM Press, New York (1988)
Brezinski, C.: Computational Aspects of Linear Control. Springer, Heidelberg (2002)
Comer, M.T., Kaltofen, E.L., Pernet, C.: Sparse polynomial interpolation and Berlekamp/Massey algorithms that correct outlier errors in input values. In: van der Hoeven, J., van Hoeij, M. (eds.) ISSAC 2012 Proceedings of the 37th International Symposium on Symbolic and Algebraic Computation, pp. 138–145. Association for Computing Machinery, New York (2012). http://www.math.ncsu.edu/~kaltofen/
Giesbrecht, M., Kaltofen, E., Lee, W.: Algorithms for computing sparsest shifts of polynomials in power, Chebychev, and Pochhammer bases. J. Symb. Comput. 36(3–4), 401–424 (2003). (Special issue International Symposium on Symbolic and Algebraic Computation (ISSAC 2002). Guest editors: Giusti, M., Pardo, L.M. http://www.math.ncsu.edu/~kaltofen/
Giesbrecht, M., Labahn, G., Lee, W.: Symbolic-numeric sparse interpolation of multivariate polynomials (extended abstract). In: Proceedings of the Ninth Rhine Workshop on Computer Algebra (RWCA’04), pp. 127–139. University of Nijmegen, The Netherlands (2004)
Giesbrecht, M., Labahn, G., Lee, W.: Symbolic-numeric sparse interpolation of multivariate polynomials. In: Dumas, J.G. (ed.) ISSAC MMVI Proceedings of the 2006 International Symposium on Symbolic and Algebraic Computation, pp. 116–123. ACM Press, New York (2006). doi: http://doi.acm.org/10.1145/1145768.1145792
Giesbrecht, M., Labahn, G., Lee, W.: Symbolic-numeric sparse interpolation of multivariate polynomials. J. Symb. Comput. 44, 943–959 (2009)
Giesbrecht, M., Roche, D.S.: Diversification improves interpolation. In: A. Leykin (ed.) Proceedings of the 2011 International Symposium on Symbolic and Algebraic Computation ISSAC 2011, pp. 123–130. Association for Computing Machinery, New York (2011)
Grigoriev, D.Y., Karpinski, M.: A zero-test and an interpolation algorithm for the shifted sparse polynomials. In: Proceedings of the AAECC-10, Lecture Notes in Computer Science, vol. 673, pp. 162–169. Springer, Heidelberg, Germany (1993)
Grigoriev, D.Y., Lakshman, Y.N.: Algorithms for computing sparse shifts for multivariate polynomials. Applic. Algebra Engin. Commun. Comput. 11(1), 43–67 (2000)
Hutton, S.E., Kaltofen, E.L., Zhi, L.: Computing the radius of positive semidefiniteness of a multivariate real polynomial via a dual of Seidenberg’s method. In: Watt [19], pp. 227–234. http://www.math.ncsu.edu/~kaltofen/
Kaltofen, E., Lakshman Y. N., Wiley, J.M.: Modular rational sparse multivariate polynomial interpolation. In: Watanabe, S., Nagata, M. (eds.) Proceedings of the 1990 International Symposium on Symbolic and Algebraic Computation (ISSAC’90), pp. 135–139. ACM Press (1990). http://www.math.ncsu.edu/~kaltofen/
Kaltofen, E., Lee, W.: Early termination in sparse interpolation algorithms. J. Symb. Comput. 36(3–4), 365–400 (2003). (Special issue International Symposium on Symbolic and Algebraic Computation (ISSAC 2002). Guest editors: Giusti, M., Pardo, L.M. http://www.math.ncsu.edu/~kaltofen/
Kaltofen, E., Yang, Z., Zhi, L.: On probabilistic analysis of randomization in hybrid symbolic-numeric algorithms. In: Verschelde, J., Watt, S.M. (eds.) SNC’07 Proceedings of the 2007 International Workshop on Symbolic-Numeric Computation, pp. 11–17. ACM Press, New York, (2007). http://www.math.ncsu.edu/~kaltofen/
Khonji, M., Pernet, C., Roch, J.L., Roche, T., Stalinsky, T.: Output-sensitive decoding for redundant residue systems. In: Watt [19], pp. 265–272
Lakshman, Y.N., Saunders, B.D.: Sparse polynomial interpolation in non-standard bases. SIAM J. Comput. 24(2), 387–397 (1995)
Lakshman, Y.N., Saunders, B.D.: Sparse shifts for univariate polynomials. Applic. Algebra Engin. Commun. Comput. 7(5), 351–364 (1996)
Prony, R.: Essai expérimental et analytique sur les lois de la Dilatabilité de fluides élastiques et sur celles de la Force expansive de la vapeur de l’eau et de la vapeur de l’alcool, à différentes températures. J. de l’École Polytechnique 1, 24–76 (1795). R. Prony is Gaspard(-Clair-François-Marie) Riche, baron de Prony
Watt, S.M. (ed.): Proceedings of the 2010 International Symposium on Symbolic and Algebraic Computation ISSAC 2010. Association for Computing Machinery, New York (2010)
Zippel, R.: Interpolating polynomials from their values. J. Symb. Comput. 9(3), 375–403 (1990)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boyer, B., Comer, M.T., Kaltofen, E.L. (2014). Sparse Polynomial Interpolation by Variable Shift in the Presence of Noise and Outliers in the Evaluations. In: Feng, R., Lee, Ws., Sato, Y. (eds) Computer Mathematics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43799-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-662-43799-5_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43798-8
Online ISBN: 978-3-662-43799-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)