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Discontinuity, Nonlinearity, and Complexity

Dimitry Volchenkov (editor), Dumitru Baleanu (editor)

Dimitry Volchenkov(editor)

Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409, USA

Email: dr.volchenkov@gmail.com

Dumitru Baleanu (editor)

Cankaya University, Ankara, Turkey; Institute of Space Sciences, Magurele-Bucharest, Romania

Email: dumitru.baleanu@gmail.com


Optimized Polynomial Interpolation

Discontinuity, Nonlinearity, and Complexity 13(2) (2024) 323--331 | DOI:10.5890/DNC.2024.06.010

C.R. Jisha, Ritesh Kumar Dubey$^{\dag}$

Department of Mathematics, SRM Institute of Science and Technology, Kattankulathur-603203, Tamilnadu, India

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Abstract

One and two-parameter families of high-degree interpolating polynomials are constructed using the Lagrange polynomial, which is closest to a given ``nice" polynomial. The problem of finding such closest polynomial is formulated as an optimization problem using the area between polynomials. The explicitly computable optimizer is uniquely deduced. Numerical results are given for high order polynomial which is closest to target piecewise linear and piecewise cubic spline interpolations. These results demonstrate that despite of high degree, the optimized polynomial is less oscillatory and mimics the ``nice" non-oscillatory property of the target polynomial.

References

  1. [1]  Gasca, M. and Sauer, T. (2000), Polynomial interpolation in several variables, Advances in Computational Mathematics, 12(4), 377-410.
  2. [2]  Celant, G. and Broniatowski, M. (2016), Interpolation and Extrapolation Optimal Designs V1: Polynomial Regression and Approximation Theory (Vol. 1), John Wiley \& Sons.
  3. [3]  Mastroianni, G. and Milovanović, G.V. (2008), Interpolation Processes: Basic Theory and Applications, Springer.
  4. [4]  Rukundo, O. and Cao, H. (2012), Nearest neighbor value interpolation. arXiv preprint arXiv:1211.1768.
  5. [5]  Ahlin, A.C. (1971), On smooth interpolation by continuously connected piecewise polynomials, Rendiconti del Circolo Matematico di Palermo, 20(2), 229-253.
  6. [6]  Alnahari, E., Shi, H., and Alkebsi, K. (2020), Quadratic interpolation based simultaneous heat transfer search algorithm and its application to chemical dynamic system optimization, Processes, 8(4), 478.
  7. [7]  Hildebrand, F.B. (1987), Introduction to Numerical Analysis, Courier Corporation.
  8. [8]  Runge, C. (1901), Über empirische Funktionen und die Interpolation, zwischen äquidistanten Ordinaten. Zeitschrift für Mathematik und Physik, 46(224-243), 20.
  9. [9]  Jerri, A.J. (1998), The Gibbs phenomenon in Fourier analysis, splines and wavelet approximations, Springer Science $\&$ Business Media, (Vol. 446)
  10. [10]  Marchi, S.D. (2022), Mapped polynomials and discontinuous kernels for runge and gibbs phenomena, In Mathematical and Computational Methods for Modelling, Approximation and Simulation (pp. 3-43), Springer, Cham.
  11. [11]  Nakatsukasa, Y., Sète, O., and Trefethen, L.N. (2018), The AAA algorithm for rational approximation, SIAM Journal on Scientific Computing, 40(3), A1494-A1522.
  12. [12]  Mallet, J.L. (1989), Discrete smooth interpolation, ACM Transactions on Graphics (TOG), 8(2), 121-144.
  13. [13]  Süli, E. and Mayers, D.F. (2003), An Introduction to Numerical Analysis, Cambridge university press.
  14. [14]  Rassias, T.M., Yanushauskas, A., and Srivastava, H.M. (Eds.) (1993), Topics in polynomials of one and several variables and their applications: volume dedicated to the memory of PL Chebyshev, 1821-1894, World Scientific.
  15. [15]  Bozorgmanesh, A.R., Otadi, M., Safe, K.A., Zabihi, F., and Barkhordari, A.M. (2009), Lagrange two-dimensional interpolation method for modeling nanoparticle formation during RESS process.
  16. [16]  Manembu, P., Kewo, A., and Welang, B. (2015), Missing data solution of electricity consumption based on Lagrange Interpolation case study: IntelligEnSia data monitoring, In 2015 International Conference on Electrical Engineering and Informatics (ICEEI) (pp. 511-516), IEEE.
  17. [17]  Jose, S.A., Raja, R., Zhu, Q., Alzabut, J., Niezabitowski, M., and Balas, V.E. (2022), An integrated eco-epidemiological plant pest natural enemy differential equation model with various impulsive strategies, Mathematical Problems in Engineering, https://doi.org/10.1155/2022/4780680.
  18. [18]  Thomas, R., Jose, S.A., Raja, R., Alzabut, J., Cao, J., and Balas, V.E. (2022), Modeling and analysis of SEIRS epidemic models using homotopy perturbation method: A special outlook to 2019-nCoV in India, International Journal of Biomathematics, 15(08), 1-1.
  19. [19]  Jose, S.A., Raja, R., Zhu, Q., Alzabut, J., Niezabitowski, M., and Balas, V.E. (2022), Impact of strong determination and awareness on substance addictions: A mathematical modeling approach, Mathematical Methods in the Applied Sciences, 45(8), 4140-4160.
  20. [20]  Jose, S.A., Ramachandran, R., Cao, J., Alzabut, J., Niezabitowski, M., and Balas, V.E. (2022), Stability analysis and comparative study on different eco‐epidemiological models: Stage structure for prey and predator concerning impulsive control, Optimal Control Applications and Methods, 43(3), 842-866.
  21. [21]  Markovsky, I. and Dörfler, F. (2022), Data-driven dynamic interpolation and approximation, Automatica, 135, 110008.
  22. [22]  Hossen, I., Anders, M.A., Wang, L., and Adam, G.C. (2022), Data-driven RRAM device models using Kriging interpolation, Scientific Reports, 12(1), 1-12.