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The paper is mainly concerned with the oscillations of numerical solutions by the linear θ − methods for a kind of linear delay differential equations with positive and negative coefficients. Some conditions under which the numerical solutions are oscillatory are obtained and it is proved that oscillations of the analytical solutions are preserved by the numerical solutions under mild conditions. Numerical experiments are given to demonstrate our results. [ABSTRACT FROM AUTHOR]

The Euler–Maruyama method is presented for linear stochastic Volterra integral equations. Then the strong convergence property is analyzed for convolution kernels and general kernels, respectively. It is well known that for stochastic ordinary differential equations, the strong convergence order of the Euler–Maruyama method is 1 2 . However, the strong superconvergence order of 1 is obtained for linear stochastic Volterra integral equations with convolution kernels if the kernel K 2 of the diffusion term satisfies K 2 ( 0 ) = 0 ; and this strong superconvergence property is inherited by linear stochastic Volterra integral equations with general kernels if the kernel K 2 of the diffusion term satisfies K 2 ( t , t ) = 0 . The theoretical results are illustrated by extensive numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we concern with the theoretical and numerical analysis of the generalized stochastic Volterra integro-differential equations (SVIDEs). The existence, uniqueness, boundedness and Hölder continuity of the analytic solutions for generalized SVIDEs are investigated. The Euler–Maruyama method for generalized SVIDEs is presented. The boundedness of the numerical solution is proved, and the strong convergence order is obtained. The theoretical results are illustrated by some numerical examples. [ABSTRACT FROM AUTHOR]