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Local accuracy and error bounds of the improved Runge-Kutta numerical methods

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Języki publikacji
EN
Abstrakty
EN
In this paper, explicit Improved Runge-Kutta (IRK) methods with two, three and four stages have been analyzed in detail to derive the error estimates inherent in them whereas their convergence, order of local accuracy, stability and arithmetic complexity have been proved in the relevant literature. Using single and multivariate Taylor series expansion for a mathematical function of one and two variables respectively, slopes involved in the IRK methods have been expanded in order to obtain the general expression for the leading or principal term in the local truncation error of the methods. In addition to this, principal error functions of the methods have also been derived using the idea of Lotkin bounds which consequently gave rise to the error estimates for the IRK methods. Later, these error estimates were compared with error estimates of the two, three, and four-stage standard explicit Runge-Kutta (RK) methods to show the better performance of the IRK methods in terms of the error bounds on the constant step-size h used for solving the initial value problems in ordinary differential equations. Finally, a couple of initial value problems have been tested to determine the maximum absolute global errors, absolute errors at the final nodal point of the integration interval and the CPU times (seconds) for all the methods under consideration to get a better idea of how the methods behave in a particular situation especially when it comes to analyzing the error terms.
Rocznik
Strony
73--84
Opis fizyczny
Bibliogr. 21 poz. tab.
Twórcy
autor
  • Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology Jamshoro, Pakistan
autor
  • Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology Jamshoro, Pakistan
autor
  • Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology Jamshoro, Pakistan
Bibliografia
  • [1] Qureshi, S., Shaikh, A.A., & Chandio, M.S. (2013). A New Iterative Integrator for Cauchy Problems. Sindh University Research Journal-SURJ (Science Series), 45(3).
  • [2] Bird, J. (2017). Higher engineering mathematics. Routledge, London.
  • [3] Mahata, A., Roy, B., Mondal, S.P., & Alam, S. (2017). Application of ordinary differential equation in glucose-insulin regulatory system modeling in fuzzy environment. Ecological Genetics and Genomics, 3, 60-66.
  • [4] Qureshi, S., Shaikh, A.A., & Chandio, M.S. (2015). Critical Study of a Nonlinear Numerical Scheme for Initial Value Problems. Sindh University Research Journal-SURJ (Science Series), 47(4).
  • [5] Qureshi, S., Chandio, M.S., Junejo, I.A., Shaikh, A.A., & Memon, Z.U.N. (2014). On Error Bound for Local Truncation Error of an Explicit Iterative Algorithm in Ordinary Differential Equations.Science International, 26(2).
  • [6] Davis, M.E. (2013). Numerical methods and modeling for chemical engineers. Courier Corporation.
  • [7] Zill, D.G. (2012). A first course in differential equations with modeling applications. Cengage Learning.
  • [8] Qureshi, S., & Ramos, H. (2018). L-stable Explicit Nonlinear Method with Constant and Variable Step-size Formulation for Solving Initial Value Problems. International Journal of Nonlinear Sciences and Numerical Simulation, 19(7-8), 741-751.
  • [9] Butcher, J.C. (2016). Numerical methods for ordinary differential equations. John Wiley & Sons.
  • [10] Musa, H., Saidu, I., & Waziri, M.Y. (2010). A simplified derivation and analysis of fourth order Runge Kutta method. International Journal of Computer Applications, 9(8), 51-55.
  • [11] Khaliq, A.Q.M. (1983). Numerical Methods for Ordinary Differential Equations (Doctoral dissertation) Department of Mathematics and Statistics, Brunel University Uxbridge, Middlesex, England.
  • [12] Qureshi, S., Sandilo, H., Sheikh, H., & Shaikh, A. (2016). Local truncation error and associated principal error function for an iterative integrator to solve Cauchy problems. Science International, 28(4).
  • [13] Hull, T.E., Enright, W.H., & Jackson, K.R. (1996). Runge-Kutta Research at Toronto. Applied Numerical Mathematics, 22(1), 225-236.
  • [14] Lotkin, M. (1951). On the accuracy of Runge-Kutta’s method. Mathematical Tables and Other Aids to Computation, 5(35), 128-133.
  • [15] Goeken, D., & Johnson, O. (1999). Fifth-order Runge-Kutta with higher order derivative approximations. Electronic Journal of Differential Equations, 2(November), 1-9.
  • [16] Ismail, M.M. (2011). Goeken-Johnson Sixth-Order Runge-Kutta Method. Journal of Education and Science, 24(49), 119-128.
  • [17] Goeken, D., & Johnson, O. (2000). Runge–Kutta with higher order derivative approximations. Applied Numerical Mathematics, 34(2-3), 207-218.
  • [18] Rabiei, F., & Ismail, F. (2011). New Improved Runge-Kutta Method with Reducing Number of Function Evaluations. In International Conference on Software Technology and Engineering, 3rd (ICSTE 2011). ASME Press.
  • [19] Ansari, M.Y., Shaikh, A.A., & Qureshi, S. (2018). Error Bounds for a Numerical Scheme with Reduced Slope Evaluations. J. Appl. Enviro. Biolog. Sci., 8, 67-76.
  • [20] Rabiei, F., Ismail, F., & Suleiman, M. (2013). Improved Runge-Kutta Methods for Solving Ordinary Differential Equations. Sains Malaysiana, 42(11), 1679-1687.
  • [21] Udwadia, F.E., & Farahani, A. (2008). Accelerated Runge-Kutta Methods. Discrete Dynamics in Nature and Society, 2008.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-740cc638-e1ca-4751-a48f-5688d10e2aaa
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