Moment inequalities for a class of stochastic systems

First moment inequalities are developed for the limiting behaviour of a class of stochastic systems, such as queues, storage or insurance-risk systems, subject to two types of input where the “primary” input is generated by a compound Poisson process and the “secondary” input by a cumulative renewal process.     

On a nonlinear stochastic integral equation with application to control systems

The aim of the present paper is to study a nonlinear stochastic integral equation of the form

Adaptive wavelet methods for the stochastic Poisson equation

We study the Besov regularity as well as linear and nonlinear approximation of random functions on bounded Lipschitz domains in ? ^d . The random functions are given either (i) explicitly in terms of a wavelet expansion or (ii) as the solution of a Poisson equation with a right-hand side in terms of a wavelet expansion. In the case (ii) we derive an adaptive wavelet algorithm that achieves the nonlinear approximation rate at a computational cost that is proportional to the degrees of freedom. These results are matched by computational experiments.     

Iterative methods for nonlinear systems associated with finite difference approach in stochastic differential equations

We present some iterative methods of different convergence orders for solving systems of nonlinear equations. Their computational complexities are studies. Then, we introduce the method of finite difference for solving stochastic differential equations of Itô-type. Subsequently, our multi-step iterative schemes are employed in this procedure. Several experiments are finally taken into account to show that the presented approach and methods work well.     

Numerical approximations for nonlinear stochastic systems with delays

We extend the numerical methods of [Kushner, H.J. and Dupuis, P., 1992 Kushner, H.J. and Dupuis, P. 2001. Numerical Methods for Stochastic Control Problems in Continuous Time, 2nd ed., Berlin and New York: Springer-Verlag. [Crossref] , [Google Scholar], Numerical Methods for Stochastic Control Problems in Continuous Time, 2nd ed., 2001 (Berlin and New York: Springer Verlag], known as the Markov chain approximation methods, to controlled general nonlinear delayed reflected diffusion models. Both the path and the control can be delayed. For the no-delay case, the method covers virtually all models of current interest. The method is robust, the approximations have physical interpretations as control problems closely related to the original one, and there are many effective methods for getting the approximations, and for solving the Bellman equation for low-dimensional problems. These advantages carry over to the delay problem. It is shown how to adapt the methods for getting the approximations, and the convergence proofs are outlined for the discounted cost function. Extensions to all of the cost functions of current interest as well as to models with Poisson jump terms are possible. The paper is particularly concerned with representations of the state and algorithms that minimize the memory requirements.     

Maximum principles for discrete-time nonlinear stochastic control systems

In this paper we treat discrete-time stochastic control systems. Using corresponding results for systems, which are linear with respect to the state variables, we derive under convexity assumptions optimality conditions in form of maximum principles     

On a stochastic nonlinear equation in one-dimensional viscoelasticity

In this paper we discuss an initial-boundary value problem for a stochastic nonlinear equation arising in one-dimensional viscoelasticity. We propose to use a new direct method to obtain a solution. This method is expected to be applicable to a broad class of nonlinear stochastic partial differential equations.

     

Optimal control over nonlinear stochastic systems

The synthesis of optimal control over nonlinear stochastic systems that are described by the Itô equations is reduced to the solution of recurrence relations derived from the Bellman stochastic equation.     

Numerical methods for nonlinear stochastic differential equations with jumps

We present and analyse two implicit methods for Ito stochastic differential equations (SDEs) with Poisson-driven jumps. The first method, SSBE, is a split-step extension of the backward Euler method. The second method, CSSBE, arises from the introduction of a compensated, martingale, form of the Poisson process. We show that both methods are amenable to rigorous analysis when a one-sided Lipschitz condition, rather than a more restrictive global Lipschitz condition, holds for the drift. Our analysis covers strong convergence and nonlinear stability. We prove that both methods give strong convergence when the drift coefficient is one-sided Lipschitz and the diffusion and jump coefficients are globally Lipschitz. On the way to proving these results, we show that a compensated form of the Euler–Maruyama method converges strongly when the SDE coefficients satisfy a local Lipschitz condition and the pth moment of the exact and numerical solution are bounded for some p>2. Under our assumptions, both SSBE and CSSBE give well-defined, unique solutions for sufficiently small stepsizes, and SSBE has the advantage that the restriction is independent of the jump intensity. We also study the ability of the methods to reproduce exponential mean-square stability in the case where the drift has a negative one-sided Lipschitz constant. This work extends the deterministic nonlinear stability theory in numerical analysis. We find that SSBE preserves stability under a stepsize constraint that is independent of the initial data. CSSBE satisfies an even stronger condition, and gives a generalization of B-stability. Finally, we specialize to a linear test problem and show that CSSBE has a natural extension of deterministic A-stability. The difference in stability properties of the SSBE and CSSBE methods emphasizes that the addition of a jump term has a significant effect that cannot be deduced directly from the non-jump literature.This work was supported by Engineering and Physical Sciences Research Council grant GR/T19100 and by a Research Fellowship from The Royal Society of Edinburgh/Scottish Executive Education and Lifelong Learning Department.     

Feedback stabilization of stochastic bilinear systems and of some nonlinear stochastic systems

The purpose of this note, is to derive sufficient conditions for the existence of stabilizing feedback laws for control stochastic bilinear systems and to apply these results to the stabilization of a class of nonlinear stochastic differential systems. The method used in this paper rely on the stochastic Lyapunov machinery     

Numerical methods for controls for nonlinear stochastic systems with delays and jumps: applications to admission control

We consider numerical methods of the Markov chain approximation type for computing optimal controls and value functions for systems governed by nonlinear stochastic delay equations. Earlier work did not allow Poisson random measure driving processes or delays that are concentrated on points with positive probability. In addition, the Poisson measures can be controlled. Previous proofs are not adequate for the present case. The algorithms are developed and convergence proved as the approximating parameters go to their limits. One motivating example concerns admissions control to a network, where the file arrival process is governed by a Poisson process, and arrivals might be admitted or not, according to the control, which leads to a controlled Poisson process. Numerical data for such an example are presented. The original problem is recast in terms of a transportation equation, which allows the development of practical algorithms. For the problems of interest, alternative methods can entail prohibitive memory and computational requirements.     

Mean-square exponential stability of stochastic theta methods for nonlinear stochastic delay integro-differential equations

This paper deals with the mean-square exponential stability of stochastic theta methods for nonlinear stochastic delay integro-differential equations. It is shown that the stochastic theta methods inherit the mean-square exponential stability property of the underlying system. Moreover, the backward Euler method is mean-square exponentially stable with less restrictions on the step size. In addition, numerical experiments are presented to confirm the theoretical results.     

A remark on triviality for the two-dimensional stochastic nonlinear wave equation

We consider the two-dimensional stochastic damped nonlinear wave equation (SdNLW) with the cubic nonlinearity, forced by a space-time white noise. In particular, we investigate the limiting behavior of solutions to SdNLW with regularized noises and establish triviality results in the spirit of the work by Hairer et al. (2012). More precisely, without renormalization of the nonlinearity, we establish the following two limiting behaviors; (i) in the strong noise regime, we show that solutions to SdNLW with regularized noises tend to 0 as the regularization is removed and (ii) in the weak noise regime, we show that solutions to SdNLW with regularized noises converge to a solution to a deterministic damped nonlinear wave equation with an additional mass term.     

A nonrandom lyapunov spectrum for nonlinear stochastic dynamical systems

We present a nonrandom version of the Multiplicative Ergodic (Oseledec) Theorem for a nonlinear stochastic dynamical system on a smooth compact Riemannian Manifold M. This theorem characterises the a.s. asymptotic behaviour of the derivative system. Our approach (based on work of Furstenberg and Kifer, who deal with a linear system) is to consider an associated system on the projective bundle over M and to relate the behaviour of the theorem to the ergodic behaviour of this system. When the system has no random element, our work reduces to an alternative approach to the Multiplicative Ergodic Theorem for a diffeomorphism of M.     

On closure methods in nonlinear stochastic dynamics

The paper discusses some problems in applications of closure techniques to moment hierarchies by considering the equation of the overdamped oscillator with cubic nonlinearity and additive Gaussian white noise. It is rigorously shown that some closure schemes can be inconsistent in spite of they good convergence properties.     

Projection solution methods for one nonlinear singular integral equation

In this paper we consider a general projection method for the solution of a nonlinear singular integral equation and its applications in the method of orthogonal polynomials, the subdomains method, and the collocation method.