Convergence analysis of an iterative algorithm to solve system of nonlinear stochastic Itô‐Volterra integral equations (original) (raw)
Related papers
A novel efficient technique for solving nonlinear stochastic Itô–Volterra integral equations
Expert Systems with Applications, 2024
There is a growing need of stochastic integral equations (SIEs) to investigate the behavior of complex dynamical systems. Since real-world phenomena frequently dependent on noise sources, modeling them naturally necessitates the use of SIEs. As most SIEs cannot be solved explicitly, thus the behaviors of the studied systems are investigated using approximate solutions of their SIEs. Despite the fact that this problem has been soundly investigated and numerous methods have been presented, the practice demonstrated that obtaining satisfied approximations is not always guaranteed, necessitating the development of new effective techniques. This paper gives a new technique for solving nonlinear Itô-Volterra SIEs by reducing them to linear or nonlinear algebraic systems via the power of a combination of generalized Lagrange functions and Jacobi-Gauss collocation points. The accuracy and reliability of the new technique are evaluated and compared with the existing techniques. Moreover, sufficient conditions to make the estimate error tends to zero are given. The new technique shows surprisingly high efficiency over the existing techniques in terms of computational efficiency and approximation capability. The accuracy of the solution based on the new technique is much higher than that via the existing techniques. The required time of the new technique is much less than that of the existing techniques, where, in some circumstances, the existing techniques take more than 20 times as long as the new technique.
A Efficient Computational Method for Solving Stochastic Itô-Volterra Integral Equations
2015
In this paper, a new stochastic operational matrix for the Legendre wavelets is presented and a general procedure for forming this matrix is given. A computational method based on this stochastic operational matrix is proposed for solving stochastic Itô-Voltera integral equations. Convergence and error analysis of the Legendre wavelets basis are investigated. To reveal the accuracy and efficiency of the proposed method some numerical examples are included.
New Numerical Method for Solving Nonlinear Stochastic Integral Equations
2020
The purpose of this paper is to propose the Chebyshev cardinal functions for solving Volterra stochastic integral equations. The method is based on expanding the required approximate solution as the element of Chebyshev cardinal functions. Though the way, a new operational matrix of integration is derived for the mentioned basis functions. More precisely, the unknown solution is expanded in terms of the Chebyshev cardinal functions including undetermined coefficients. By substituting the mentioned expansion in the original problem, the operational matrix reducing the stochastic integral equation to system of algebraic equations. The convergence and error analysis of the etablished method are investigated in Sobolev space. The method is numerically evaluated by solving test problems caught from the literature by which the computational efficiency of the method is demonstrated. From the computational point of view, the solution obtained by this method is in excellent agreement with th...
International Journal of Dynamical Systems and Differential Equations, 2021
In this paper, we give a new method for solving stochastic nonlinear Volterra integral equations by using shifted Legendre operational matrix. It is discussed that how the stochastic differential equations (SDE) could numerically be solved as matrix problems. By using this new operational matrix of integration and the so-called collocation method, nonlinear Volterra integral equations is reduced to systems of algebraic equations with unknown Legendre coefficients. Finally, the high accuracy of approximated solutions are illustrated by several experiment.
Numerical Approximation of Nonlinear Stochastic Volterra Integral Equation using Walsh Function
arXiv (Cornell University), 2023
This paper adopts a highly effective numerical approach for approximating non-linear stochastic Volterra integral equations (NLSVIEs) based on the operational matrices of the Walsh function and the collocation method. The method transforms the integral equation into a system of algebraic equations, which allows for the derivation of an approximate solution. Error analysis has been performed, confirming the effectiveness of the proposed method, which results in a linear order of convergence. Numerical examples are provided to illustrate the precision and effectiveness of this proposed method.
Collocation methods for nonlinear stochastic Volterra integral equations
Computational and Applied Mathematics
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.
2012
In this paper, we obtain stochastic operational matrix of block pulse functions on interval [0, 1) to solve stochastic Volterra-Fredholm integral equations. By using block pulse functions and their stochastic operational matrix of integration, the stochastic Volterra-Fredholm integral equation can be reduced to a linear lower triangular system which can be directly solved by forward substitution. We prove that the rate of convergence is O(h). Furthermore, the results show that the approximate solutions have a good degree of accuracy.
Numerical Approximation of Stochastic Volterra-Fredholm Integral Equation using Walsh Function
arXiv (Cornell University), 2023
In this paper, a computational method is developed to find an approximate solution of the stochastic Volterra-Fredholm integral equation using the Walsh function approximation and its operational matrix. Moreover, convergence and error analysis of the method is carried out to strengthen the validity of the method. Furthermore, the method is numerically compared to the block pulse function method and the Haar wavelet method for some non-trivial examples.
Numerical Approximation of Stochastic Volterra Integral Equation Using Walsh Function
arXiv (Cornell University), 2023
This paper provides a numerical approach for solving the linear stochastic Volterra integral equation using Walsh function approximation and the corresponding operational matrix of integration. A convergence analysis and error analysis of the proposed method for stochastic Volterra integral equations with Lipschitz functions are presented. Numerous examples with available analytical solutions demonstrate that the proposed method solves linear stochastic Volterra integral equations more precisely than existing techniques. In addition, the numerical behaviour of the method for a problem with no known analytical solution is demonstrated.
2016
A Legendre wavelet method is presented for numerical solutions of stochastic Volterra-Fredholm integral equations. The main characteristic of the proposed method is that it reduces stochastic Volterra-Fredholm integral equations into a linear system of equations. Convergence and error analysis of the Legendre wavelets basis are investigated. The efficiency and accuracy of the proposed method was demonstrated by some non-trivial examples and comparison with the block pulse functions method.