Abstract
Influenced by Xiao et al. (J Integral Equations Appl 30(1):197–218, 2018), collocation methods are developed to study strong convergence orders of numerical solutions for nonlinear stochastic Volterra integral equations under the Lipschitz condition in this paper. Some properties of exact solutions are discussed. These properties include the mean-square boundedness, the Hölder condition, and conditional expectations. In addition, this paper considers the solvability, the mean-square boundedness, and strong convergence orders of numerical solutions. At last, we validate our conclusions by numerical experiments.
Similar content being viewed by others
References
Babolian E, Shamloo AS (2008) Numerical solution of Volterra integral and integro-differential equations of convolution type by using operational matrices of piecewise constant orthogonal functions. J Comput Appl Math 214(2):495–508
Burrage P, Burrage K (2002) A variable stepsize implementation for stochastic differential equations. SIAM J Sci Comput 24(3):848–864
Cao Y, Zhang R (2015) A stochastic collocation method for stochastic Volterra equations of the second kind. J Integral Equations Appl 27(1):1–25
Conte D, Paternoster B (2009) Multistep collocation methods for Volterra integral equations. Appl Numer Math 59(8):1721–1736
Higham D, Mao X, Stuart A (2002) Strong convergence of Euler-type methods for nonlinear stochastic differential equations. SIAM J Numer Anal 40(3):1041–1063
Itô K (1979) On the existence and uniqueness of solutions of stochastic integral equations of the Volterra type. Kodai Math J 2:158–170
Maleknejad K, Almasieh H, Roodaki M (2010) Triangular functions (TF) method for the solution of nonlinear Volterra–Fredholm integral equations. Commun Nonlinear Sci Numer Simul 15(11):3293–3298
Maleknejad K, Dehbozorgi R (2018) Adaptive numerical approach based upon Chebyshev operational vector for nonlinear Volterra integral equations and its convergence analysis. J Comput Appl Math 344:356–366
Maleknejad K, Khodabin M, Rostami M (2012a) A numerical method for solving m-dimensional stochastic Ito–Volterra integral equations by stochastic operational matrix. Comput Math Appl 63(1):133–143
Maleknejad K, Khodabin M, Rostami M (2012b) Numerical solution of stochastic Volterra integral equations by a stochastic operational matrix based on block pulse functions. Math Comput Model 55:791–800
Maleknejad K, Khodabin M (2014) Shekarabi FH (2014) Modified block pulse functions for numerical solution of stochastic volterra integral equations. J Appl Math 2014(3):1–10
Maleknejad K, Sohrabi S, Rostami Y (2007) Numerical solution of nonlinear Volterra integral equations of the second kind by using Chebyshev polynomials. Appl Math Comput 188(1):123–128
Mao X (2007) Stochastic differential equations and applications. Horwood, Chichester
Mao X, Szpruch L (2012) Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients. J Comput Appl Math 238:14–28
Mirzaee F, Hadadiyan E (2014) A collocation technique for solving nonlinear stochastic Ito–Volterra integral equations. Appl Math Comput 247:1011–1020
Mohammadi F (2015) A wavelet-based computational method for solving stochastic Ito–Volterra integral equations. J Comput Phys 298:254–265
Roemisch W, Winkler R (2006) Stepsize control for mean-square numerical methods for stochastic differential equations with small noise. SIAM J Sci Comput 28(2):604–625
Shekarabi FH, Khodabin M, Maleknejad K (2007) Stochastic differential equations and applications, 2nd edn. Horwood, Chichester
Sheng CT, Wang ZQ, Guo BY (2014) A multistep Legendre–Gauss spectral collocation method for nonlinear Volterra integral equations. SIAM J Numer Anal 52(4):1953–1980
Tretyakov MV, Zhang Z (2013) A fundamental mean-square convergence theorem for SDEs with locally lipschitz coefficients and its applications. SIAM J Numer Anal 51(6):3135–3162
Valinejad A, Hosseini SM (2010) A variable step-size control algorithm for the weak approximation of stochastic differential equations. Numer Algorithms 55(4):429–446
Wang ZD (2008) Existence and uniqueness of solutions to stochastic Volterra equations with singular kernels and non-lipschitz coefficients. Stat Prob Lett 78(9):1062–1071
Xiao Y, Zhang H (2011) Convergence and stability of numerical methods with variable step size for stochastic pantograph differential equations. Int J Comput Math 88(14):2955–2968
Xiao Y, Shi JN, Yang ZW (2018) Split-step collocation methods for stochastic Volterra integral equations. J Integr Equations Appl 30(1):197–218
Acknowledgements
The authors would like to thank Yang Zhanwen (School of Mathematics, Harbin Institute of Technology, Harbin, 150001, P.R. China.) and Yin Zhi (Institute of Mathematics, Harbin Institute of Technology, Harbin, 150001, P.R. China) for their valuable advice and comments to improve the quality of this work. We also thank reviewers for their valuable advice and comments to improve the quality of this work.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Hui Liang.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Xu, X., Xiao, Y. & Zhang, H. Collocation methods for nonlinear stochastic Volterra integral equations. Comp. Appl. Math. 39, 330 (2020). https://doi.org/10.1007/s40314-020-01353-x
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40314-020-01353-x