# Numerical Study of Random Periodic Lipschitz Shadowing of Stochastic Differential Equations

Authors: Qingyi Zhan; Xiangdong Xie
Journal: Discrete Dynamics in Nature and Society
 (2018)

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## Abstract

This paper is devoted to a new numerical approach for the possibility of(ω,Lδ)-periodic Lipschitz shadowing of a class of stochastic differential equations. The existence of (ω,Lδ)-periodic Lipschitz shadowing orbits and expression of shadowing distance are established. The numerical implementation approaches to the shadowing distance by the random Romberg algorithm are presented, and the convergence of this method is also proved to be mean-square. This ensures the feasibility of the numerical method. The practical use of these theorems and the associated algorithms is demonstrated in the numerical computations of the (ω,Lδ)-periodic Lipschitz shadowing orbits of the stochastic logistic equation.


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## Body

## 1. Introduction

The investigation of random periodic orbits at large and in specific stochastic differential equations (SDEs) is a difficult dynamical problem [1]. In general, numerical computation is still one of the most feasible methods of studying random periodic orbits of chaotic systems of SDEs whose applications describe many natural phenomena in meteorology, biology, and so on [2, 3].Due to the sensitivity of the initial value and random noise pumped into the systems constantly, it is difficult to expect that a particular solution of chaotic systems of SDEs can be well approximated by a numerical solution for any given length of time. Therefore, it is always difficult to infer rigorously the existence of a random periodic orbit from the numerical computations. Shadowing property has an important position in theory and application of random dynamical systems (RDS), especially in the numerical simulations of chaotic systems of SDEs. We present here a new method for establishing the existence of a true random periodic orbit of SDEs which lies near a computed random periodic orbit.In this work the main motivations are twofold. On the one hand, it follows from the classical results about random periodic solutions of SDEs [1, 4] that the numerical simulation of random periodic solution has been performed by the author; see [5] and references therein. Here random periodic solution is a special class of periodic solution with random noise inputted, and the detail is shown as Remark 1. This provides the foundation of numerical analysis. On the other hand, it has been inspired by our earlier work [6, 7] on shadowing orbits of SDEs where we establish random shadowing in a rather general setting.For example, the conditions which can assure the stochastic shadowing in a class of SDEs have been constructed in [6, 7]. Liu et al. have made useful contributions to the numerical analysis of RDS [4, 8, 9]. There are some work on shadowing orbits of discrete random dynamical systems generated by random iterations [10, 11]. As we know, these papers explicitly utilize the shadowing assumptions which have no constructions. To the best of our knowledge, up to now there have been less investigations of the random periodic Lipschitz shadowing of SDEs in the literature. Shadowing is still an interesting method for studying their random periodic dynamic behavior of SDEs.It is well known that Lipschitz shadowing is the extension of classical shadowing, which is widely used in numerical analysis and computation. Therefore it is very meaningful to extend this definition to the stochastic periodic case, which is defined as(ω,Lδ)-periodic Lipschitz shadowing orbits (RPLSO). Then we only need to construct some conditions such that the systems of SDEs possess RPLSO. In fact, these conditions can guarantee that the systems of SDEs have the stochastic periodic Lipschitz shadowing implicitly. Therefore, this provides an important method which is used in the proof of the existence of RPLSO, and it follows from stochastic calculus that the shadowing distance can be determined for any given (ω,δ)-pseudoperiodic orbit. This is the essence of the stochastic periodic Lipschitz shadowing which has been investigated from such practical point of view. And this brings great convenience to numerical analysis, so it can be an available and realistic method of estimating shadowing distance, that is, the maximum distance between an (ω,δ)-pseudoperiodic orbit and its corresponding nearest true random periodic orbit in mean-square sense.Utilizing forward infinite horizon stochastic integral equations, we propose the finite-time random periodic Lipschitz shadowing theorem of SDEs. By random Romberg algorithm and random numerical computation, the shadowing distance is obtained. These results show that under some appropriate conditions the numerical approximative random periodic orbits of SDEs are close to the true ones and shadowing distance can be well estimated.A more detailed outline of this paper is as follows. Section2 deals with some preliminaries addressed to clarify the presentation of concepts and norms used later. Section 3 is devoted to the feasibility analysis of the finite-time (ω,Lδ)-periodic Lipschitz shadowing. Section 4 presents the numerical analysis of shadowing distance which contains the detailed numerical implementation method and its convergence analysis. Illustrative numerical experiments using the well-known stochastic logistic equation for the main results are included in Section 5. Section 6 summarizes the conclusions of this article.


## 2. Preliminaries

Let(Ω,F,P) be a Wiener space, W(t)(t∈R) be a standard k dimensional Wiener process, and {Ft}t∈R be its natural normal filtration. And Ω={ω∈C(R,Rk):ω(0)=0} which means that the elements of Ω can be identified with paths of a Wiener process ω(t)=Wt(ω). We consider a class of Ito SDEs of the form(1)dXt=-AXtdt+ft,Xtdt+gtdWt,X0=X0∈Rd,where Xt:Ω→Rd, f:R×Rd→Rd, g:R→Rd×k, A is a d×d hyperbolic matrix, whose real part of the eigenvalue is positive, and we define that Tt=e-At is a pseudohyperbolic linear flow induced by -A; the initial value X0 is independent of F0 and satisfies the inequality EX02<∞.


### 2.1. Basic Assumptions and Notations

In this paper, we make the following assumptions which are made for the theoretical analysis.Hypothesis 1 (see [6]).
(i) The initial valueX0 is bounded; that is, X0≤K for K>0.
(ii) Assume that the functionf:R×Rd→Rd is continuous, measurable function, and the function g:R→Rd is continuous too. Suppose that there exists a constant τ>0 such that for ∀t∈R and ∀X∈Rd, (2)ft,X=ft+τ,X,gt=gt+τ.
(iii) The functionf is locally bounded, locally Lipschitz with respect to the second variable, and is a C1 vector field on Rd. That is, there exist positive constants J,M, and K1 such that f(t,0)≤J holds for t∈R and ∀X,Y≤M; then(3)ft,X-ft,Y≤K1X-Y.
(iv) The functiong is globally bounded. That is, there exists a constant K2>0 such that gt≤K2 holds for t∈R.We define(4)θ:R×Ω⟶Ω,θtωs=Wt+s-Wt,and s≤t, s∈R, t∈R. By the conclusions in [2], SDE (1) generates a stochastic flow φ:R×R×Ω×Rd→Rd when the solution of SDE (1) exists uniquely, which is usually written as φ(s,t,ω,X)≔φ(s,t,ω)X on the metric dynamical systems (Ω,F,P,θt). The stochastic flow φ is given by(5)φs,t,ωX=X+∫st-Aφs,r,ωX+fr,φs,r,ωXdr+∫stgφs,r,ωX,rdWr,t≥s.Remark 1.
Here the expressionφ(s,t,ω)X denotes a solution φ of SDE (1) at the time t which has the sample ω at the initial time s and initial value X. And the random periodic solution is defined as below. If U(t,ω) is a random periodic solution of φ(s,t,ω)X, then we obtain that(6)φt,t+τ,ωUt,ω=Ut+τ,ω=Ut,θτω,for any t∈R+ and ω∈Ω. Furthermore, in this paper, we only consider the case that A is a deterministic matrix. Because it needs complex theory and tools, the random case A(ω) will be investigated in our future work.Remark 2.
It follows from the conclusions in [2, 12] that the definition of RDS is the extension of the definition of stochastic flow; that is, the latter is a particular case of the former. Therefore, some conclusions in [6] are also valid in this article.We also utilize the notations as follows.(i)
For any random vectorX=(X1,X2,…,Xd)∈L2(Ω), we define (7)X2=∫ΩX1ω2+X2ω2+⋯+Xdω2dPω1/2<∞.(ii)
For a stochastic processX(t,ω) with Xt(ω)∈L2(Ω) and t∈R, we define (8)Xt,ω2=supt∈RXtω2<∞.(iii)
The norm of random matrix is defined in the form of(9)BL2Ω=EB21/2,whereB is a random matrix and · is the operator norm.(iv)
In the continuation the norm∥·∥2 and ∥·∥L2(Ω) are written as ∥·∥ unless otherwise stated.


### 2.2. Some Concepts and Remarks

We will extend the definitions of(ω,δ)-pseudoorbit and (ω,ε)-shadowing in [6] to the random periodic case, which also referr to the definitions of pseudoperiodic orbit and periodic shadowing in [7, 13].Definition 3.
For a given positive numberδ, if there is a sequence of positive times {tk}k=0N+1, 0≤t0≤t1≤⋯≤τ≤tN+1, τ and a sequence of random variables(10)Ykθtkω,Ftkk=0N,which means that Yk(θtkω) is Ftk-adapted for k=0,1,2,…,N and ω∈Ω, such that the following inequalities hold(11)Yk+1θtk+1ω-φtk,tk+1,θtkωYkθtkω≤δ,k=0,1,…,N-1,YNθtNω-Y0θt0ω≤δ,then the random variables {(Yk(θtkω),Ftk)}k=0N are said to be a (ω,δ)-pseudorandom periodic orbit of SDE (1) in mean-square sense, where the inequality describes the random periodic property, and it is an extension of the corresponding definitions; we refer for the details to [6, 7, 13].Definition 4 (see [14]).
For a given positive numberδ0>0,L>0, and any (ω,δ)-pseudorandom periodic orbit {Ykθtkω,Ftk}k=0N(0<δ≤δ0) of SDE (1) with associated times {tk}k=0N+1, if there is a sequence of times {hk}k=0N+1, h0≤h1≤⋯≤τ≤hN+1, and random variables {(Xk(θhkω),Fhk)}k=0N, such that the following inequalities hold(12)Ykθtkω-Xkθhkω≤Lδ,0≤tk-hk≤Lδ≔ε,k=0,1,…,N,and random variables {(Xk(θhkω),Fhk)}k=0N are on the true orbits of SDE (1), that is(13)Xk+1θhk+1ω=φhk,hk+1,θhkωXkθhkω,k=0,1,2,…,N-1,(14)X0θh0ω=φhN,hN+1,θhNωXNθhNω,then the (ω,δ)-pseudorandom periodic orbit {(Yk(θtkω),Ftk)}k=0N is said to be (ω,Lδ)-periodic Lipschitz shadowed by a true orbit of SDE (1) in mean-square sense.Remark 5.
As theσ-algebra Ft(t≥0) is nondecreasing, in order to guarantee the random variable Xk(θhkω)(k=0,1,2,…,N) is Ftk-measurable, we need the shadowing condition 0≤tk-hk≤ε instead of |tk-hk|≤ε for deterministic counterpart [2]. We choose a sequence of times {hk}k=0N+1={tk}k=0N+1 in sequels.Remark 6.
(
ω
,
L
δ
)-periodic Lipschitz shadowing is a special case of (ω,δ)-periodic shadowing; that is, the former obtains an explicit dependent relationship between the local error and the shadowing distance, but the latter does not.


## 2.1. Basic Assumptions and Notations

In this paper, we make the following assumptions which are made for the theoretical analysis.Hypothesis 1 (see [6]).
(i) The initial valueX0 is bounded; that is, X0≤K for K>0.
(ii) Assume that the functionf:R×Rd→Rd is continuous, measurable function, and the function g:R→Rd is continuous too. Suppose that there exists a constant τ>0 such that for ∀t∈R and ∀X∈Rd, (2)ft,X=ft+τ,X,gt=gt+τ.
(iii) The functionf is locally bounded, locally Lipschitz with respect to the second variable, and is a C1 vector field on Rd. That is, there exist positive constants J,M, and K1 such that f(t,0)≤J holds for t∈R and ∀X,Y≤M; then(3)ft,X-ft,Y≤K1X-Y.
(iv) The functiong is globally bounded. That is, there exists a constant K2>0 such that gt≤K2 holds for t∈R.We define(4)θ:R×Ω⟶Ω,θtωs=Wt+s-Wt,and s≤t, s∈R, t∈R. By the conclusions in [2], SDE (1) generates a stochastic flow φ:R×R×Ω×Rd→Rd when the solution of SDE (1) exists uniquely, which is usually written as φ(s,t,ω,X)≔φ(s,t,ω)X on the metric dynamical systems (Ω,F,P,θt). The stochastic flow φ is given by(5)φs,t,ωX=X+∫st-Aφs,r,ωX+fr,φs,r,ωXdr+∫stgφs,r,ωX,rdWr,t≥s.Remark 1.
Here the expressionφ(s,t,ω)X denotes a solution φ of SDE (1) at the time t which has the sample ω at the initial time s and initial value X. And the random periodic solution is defined as below. If U(t,ω) is a random periodic solution of φ(s,t,ω)X, then we obtain that(6)φt,t+τ,ωUt,ω=Ut+τ,ω=Ut,θτω,for any t∈R+ and ω∈Ω. Furthermore, in this paper, we only consider the case that A is a deterministic matrix. Because it needs complex theory and tools, the random case A(ω) will be investigated in our future work.Remark 2.
It follows from the conclusions in [2, 12] that the definition of RDS is the extension of the definition of stochastic flow; that is, the latter is a particular case of the former. Therefore, some conclusions in [6] are also valid in this article.We also utilize the notations as follows.(i)
For any random vectorX=(X1,X2,…,Xd)∈L2(Ω), we define (7)X2=∫ΩX1ω2+X2ω2+⋯+Xdω2dPω1/2<∞.(ii)
For a stochastic processX(t,ω) with Xt(ω)∈L2(Ω) and t∈R, we define (8)Xt,ω2=supt∈RXtω2<∞.(iii)
The norm of random matrix is defined in the form of(9)BL2Ω=EB21/2,whereB is a random matrix and · is the operator norm.(iv)
In the continuation the norm∥·∥2 and ∥·∥L2(Ω) are written as ∥·∥ unless otherwise stated.


## 2.2. Some Concepts and Remarks

We will extend the definitions of(ω,δ)-pseudoorbit and (ω,ε)-shadowing in [6] to the random periodic case, which also referr to the definitions of pseudoperiodic orbit and periodic shadowing in [7, 13].Definition 3.
For a given positive numberδ, if there is a sequence of positive times {tk}k=0N+1, 0≤t0≤t1≤⋯≤τ≤tN+1, τ and a sequence of random variables(10)Ykθtkω,Ftkk=0N,which means that Yk(θtkω) is Ftk-adapted for k=0,1,2,…,N and ω∈Ω, such that the following inequalities hold(11)Yk+1θtk+1ω-φtk,tk+1,θtkωYkθtkω≤δ,k=0,1,…,N-1,YNθtNω-Y0θt0ω≤δ,then the random variables {(Yk(θtkω),Ftk)}k=0N are said to be a (ω,δ)-pseudorandom periodic orbit of SDE (1) in mean-square sense, where the inequality describes the random periodic property, and it is an extension of the corresponding definitions; we refer for the details to [6, 7, 13].Definition 4 (see [14]).
For a given positive numberδ0>0,L>0, and any (ω,δ)-pseudorandom periodic orbit {Ykθtkω,Ftk}k=0N(0<δ≤δ0) of SDE (1) with associated times {tk}k=0N+1, if there is a sequence of times {hk}k=0N+1, h0≤h1≤⋯≤τ≤hN+1, and random variables {(Xk(θhkω),Fhk)}k=0N, such that the following inequalities hold(12)Ykθtkω-Xkθhkω≤Lδ,0≤tk-hk≤Lδ≔ε,k=0,1,…,N,and random variables {(Xk(θhkω),Fhk)}k=0N are on the true orbits of SDE (1), that is(13)Xk+1θhk+1ω=φhk,hk+1,θhkωXkθhkω,k=0,1,2,…,N-1,(14)X0θh0ω=φhN,hN+1,θhNωXNθhNω,then the (ω,δ)-pseudorandom periodic orbit {(Yk(θtkω),Ftk)}k=0N is said to be (ω,Lδ)-periodic Lipschitz shadowed by a true orbit of SDE (1) in mean-square sense.Remark 5.
As theσ-algebra Ft(t≥0) is nondecreasing, in order to guarantee the random variable Xk(θhkω)(k=0,1,2,…,N) is Ftk-measurable, we need the shadowing condition 0≤tk-hk≤ε instead of |tk-hk|≤ε for deterministic counterpart [2]. We choose a sequence of times {hk}k=0N+1={tk}k=0N+1 in sequels.Remark 6.
(
ω
,
L
δ
)-periodic Lipschitz shadowing is a special case of (ω,δ)-periodic shadowing; that is, the former obtains an explicit dependent relationship between the local error and the shadowing distance, but the latter does not.


## 3. Feasibility Analysis of(ω,Lδ)-Periodic Lipschitz Shadowing

Theorem 7.
Suppose thatA is a class of hyperbolic d×d matrix and the eigenvalues of A are denoted by {λj,j=1,2,…,d} which satisfy 0<Re(λ1)≤Re(λ2)≤⋯≤Re(λd). Let {(Yk(θtkω),Ftk)}k=0N be a bounded (ω,δ)-pseudorandom periodic orbit of SDE (1), 0<δ<δ0. Moreover, suppose that SDE (1) satisfies Hypothesis 1 and the maximum of the locally Lipschitz constant of f be K1∈[0,2Re(λd)) with respect to the (ω,δ)-pseudorandom periodic orbit of SDE (1) {(Yk(θtkω),Ftk)}k=0N.
Then there exists a sequence of points{(Xk(θhkω),FtN)}k=0N and a constant L>0 such that the (ω,δ)-pseudorandom periodic orbit {(Yk(θtkω),Ftk)}k=0N is (ω,Lδ)-periodic Lipschitz shadowed by a true orbit of SDE (1) which contains this sequence of points {(Xk(θhkω),Ftk)}k=0N in mean-square sense, where times hk=tk, k=0,1,…,N+1.Proof.
Firstly, for a given constantδ0 and a bounded (ω,δ)-pseudoperiodic orbit of SDE (1) {(Yk(θtkω),Ftk)}k=0N, if we choose any δ∈(0,δ0) and an initial value X¯t0 such that X¯t0-Y0(θt0ω)≤δ, then we can prove that SDE (1) with initial value X¯t0 has a unique random periodic solution Y(t,ω):(-∞,+∞)×Ω→Rd, and that Y(t,ω) is a solution of the forward infinite horizon integral equation(15)Yt,ω=∫-∞tTt-rfr,Yr,ωdr+ω∫-∞tTt-rgrdWr.
This claim is proved by a truncation procedure. As we have done in [3], we only need to take the limit as J→+∞. For each J≥1 and J∈Z, define the truncation function (16)fJt,X=ft,X,ifX≤J,ft,JXX,ifX>J.Then it follows from Theorem  3.3 in [5] that there is a unique random periodic solution XtJ≔YJ(t,ω) to the equation(17)dXtJ=-AXtJdt+fJt,XtJdt+gtdWt,XJ0=X0∈Rd.Define the stopping time(18)τJ=tN+1∧inf⁡t∈0,tN+1,XtJ≥J.We can show that(19)XtJ=XtJ+1,if0≤t≤τJ.This implies that τJ is increasing. Then we can use the linear growth condition to prove that for almost all ω∈Ω, there exists an integer J0=J0(ω) such that τJ=tN+1 whenever J≥J0.
Now we defineXt by(20)Xt=XtJ0,t∈0,tN+1.It follows from (19) that we have X(t∧τJ)=X(t∧τJ)J, and by (17) we obtain that(21)dXt∧τJJ=-AXt∧τJJdt+fJt,Xt∧τJJdt+gtdWt.Let J→∞; we see that Xt is a solution of SDE (1). Therefore, this proved the existence of the true orbit of SDE (1) with proper modified initial conditions.
Secondly, we only need to prove that a(ω,δ)-pseudoperiodic orbit {(Yk(θtkω),Ftk)}k=0N is (ω,Lδ)-periodic Lipschitz shadowed by this sequence of points {(Xk(θhkω),Ftk)}k=0N, which lie on a true orbit of SDE (1); that is, we only need to prove that (14) holds.
It follows from the above that the random periodic solutionY(t,ω) of SDE (1) exists and its expression can be provided as (15). In the finite-time interval [0,tN+1] we can choose δ∈(0,δ0), and the distance Δk between the (ω,δ)-pseudoperiodic orbit {(Yk(θtkω),Ftk)}k=0N and the points on random periodic solution Y(t,ω) at the time {tk}k=0N+1 is a finite constant Δk. With this method we can choose(22)L≔max0≤k≤NΔkδas the Lipschitz constant and(23)Δ=max0≤k≤NΔkas the radius of (ω,δ)-pseudoperiodic orbit’s neighbourhood. Therefore, the conclusion is immediate from Definition 4. This completes the proof of Theorem 7.Remark 8.
The difference from [5] is that the space is Rd, not a subspace B⊂Rd.


## 4. Numerical Implementation of Shadowing Distance

### 4.1. A Detailed Implementation Method of Shadowing Distance

We show the numerical method in detail, which we use for the approximation of shadowing distance, and this method consists of three steps as follows.Step 1.
Utilizing the one-step numerical scheme (Euler-Maruyama (EM) scheme, Milstein scheme [15]) to solve the following equation from tk to tk+1 with the initial values X(0)=X0,(24)dXt=-AXtdt+ft,Xtdt+gtdWt,we obtain the approximations of Xk+1, that is, (ω,δ)-pseudoperiodic orbit of SDE (1), (25)Xk+1=Xk+-AXk+ftk,Xktk+1-tk+gtkΔWk.Step 2.
It follows from Theorem7 that the forward infinite horizon integral equation (15) is the random periodic solution of SDE (1) with new initial conditions which is chosen as Section 3. It follows from Theorem  3.4 in [5] that the random periodic solution (15) is mean-square uniformly asymptotically stable.In order to make this article self-contained we outline the numerical method which is used for approximating the random periodic solution. The reader is referred to the paper [5] for a more detailed description. Therefore, numerical implementation method of the random periodic orbits is described as follows.Firstly, we need to obtain the initial value for the sake of the approximation. It follows from (15) that we get(26)Y0,ω=∫-∞0T-rfr,Yr,ωdr+ω∫-∞0T-rgrdWr.For any given presupposed error tolerance δ^∈(0,Δt], if s′<0 is chosen such that the following inequality holds(27)Y0,ω-∫s′0T-rfr,Yr,ωdr-ω∫s′0T-rgrdWr≤δ^,then the Ito integral Y¯(0,ω) can be used to approximate the improper integral (26), where(28)Y¯0,ω=∫s′0T-rfr,Y¯r,ωdr+ω∫s′0T-rgrdWr,and Δt≔tN+1/N′. Therefore, Y¯(0,ω) can be chosen as the approximating initial value in the given presupposed error tolerance.Secondly, we need to obtain the approximation of the improper integral (15). Therefore the improper integral (15) in the finite-time interval [0,tN+1] can be approximated by the Ito integral (29)(29)Y~tN+1,ω=∫0tN+1Tt-rfr,Y~r,ωdr+ω∫0tN+1Tt-rgrdWr,with initial value Y¯(0,ω) at the time t=0. It follows from (29), pullback theory, and the construction of the random periodic orbits that Y~(tN+1,ω) is ω-wise, too. Here, pullback theory is pullback random analysis theory which is shown as [1] in detail. Furthermore, Y~(tN+1,ω) and Y¯(0,ω) have the same sample ω at the time t=0.Remark 9.
By means of reselecting the corresponding starting time ands′, we can simulate a random periodic solution in an arbitrary finite-time interval with any given presupposed error tolerance.Finally, in order to improve the accuracy of the integral, the random Romberg algorithm is applied to (29) and Y¯(0,ω). The method applied to (29) is shown as follows briefly.LetY~n(t,ω) be the approximation of Y~(0,nΔt,ω); then we can obtain(30)Y~nt,ω=∫0nΔtTnΔt-rfr,Y~n-1r,ωdr+ω∫0nΔtTnΔt-rgrdWr,where n=1,…,N′ and Y~N′(t,ω) is the numerical approximation of Y~(tN+1,ω).For any given presupposed error toleranceδ~∈[0,δ^], if the following inequality holds(31)Rj,j-1-Rjj≤δ~,the computation of the random Romberg algorithm is ended and Rjj is viewed as the approximation of (30). That is,(32)Y~nt,ω=Rjj,where Rj,j-1 and Rjj are obtained by the implementation of the random Romberg algorithm; this algorithm is shown in detail in [5].Step 3.
It follows from Steps1 and 2 and Theorem 7 that we can obtain the approximation of shadowing distance and the Lipschitz constant of Lipschitz shadowing as follows:(33)Δ=maxnXn-Y~nt,ω+δ^,L≔Δδ.


### 4.2. Convergence Analysis

We can divide the time interval[0,tN+1] into N′ subintervals with the length Δt. Then we obtain the exact solution of SDE (1) in [0,tN+1] as follows:(34)Yˇnt,ω=∫0nΔte-AnΔt-rfr,Yˇnr,ωdr+ω∫0nΔte-AnΔt-rgrdWr.Therefore the theoretical shadowing distance is shown as follows:(35)Δ¯=maxnXn-Yˇnt,ω+δ^.We will show that the shadowing distance (33) is bounded. And the numerical approximation (33) to shadowing distance is mean-square convergent to the theoretical result (35).Theorem 10.
Suppose thatX0∈L2(Ω), SDE (1) satisfy the condition of Theorem 7, then the shadowing distance (33) is bounded, and the numerical approximation (33) to shadowing distance is mean-square convergent to the theoretical result (35) by the random Romberg algorithm.Proof.
First and foremost, it follows from (30) that(36)EXn+1-Y~n+1t,ω2≤EI-tn+1-tnAXn-∫0n+1Δtfr,Xn-Tn+1Δt-rfr,Y~nr,ωdr-ω∫0n+1Δt1-Tn+1Δt-rgrdWr2≤I1+I2+I3,where(37)I1=3EI-tn+1-tnAXn2,I2=3E∫0n+1Δtfr,Xn-Tn+1Δt-rfr,Y~nr,ωdr2,I3=3Eω∫0n+1Δt1-Tn+1Δt-rgrdWr2.Utilizing the boundedness of X0, there exists a positive constant M1 such that I1≤M1Δt. By the conclusion, we obtain that 0<T(n+1)Δt-r<1. Then it follows from the integral property and the Lipschitz condition of the function f that(38)I2≤3E∫0n+1Δtfr,Xn-fr,Y~nr,ωdr2≤3K12∫0n+1ΔtEXn-Y~nr,ω2dr.It follows from Ito isometry and the boundness condition of the function g that (39)I3≤3∫0n+1ΔtEgr2dr≤3K22n+1Δt.Therefore by the Gronwall inequality, there exists a number M2 such that (36) implies that(40)Δn=EXn-Y~nt,ω21/2+δ^≤M2,where M2=M1+3K22nΔt·exp⁡3K12nΔt.
By the conclusions (40), the first conclusion of Theorem 10 holds; that is, the shadowing distance Δn is bounded.
Secondly, from the expression ofY~n(t,ω), we obtain(41)Y~nt,ω=∫0nΔte-AnΔt-rfr,Y~n-1r,ωdr+ω∫0nΔte-AnΔt-rgrdWr.Then it implies that(42)EΔ-Δ¯2≤maxn⁡EYˇnt,ω-Y~nt,ω2.We let(43)I4≔EYˇnt,ω-Y~nt,ω2,and we obtain that(44)I4=E∫0nΔte-AnΔt-rfr,Yˇnr,ω-fr,Y~n-1r,ωdr2.It follows from the Cauchy-Schwarz inequality that(45)I4≤∫0nΔte-2AnΔt-rdr·∫0nΔtK12EYˇnr,ω-Y~n-1r,ω2dr≤K3K12∫0nΔtEYˇnr,ω-Y~n-1r,ω2dr,where K3=∫0tN+1e-2A(t-r)dr.
Then the fact that(a+b)2≤2a2+2b2, a,b∈R, implies that(46)I4≤2K3K12∫0nΔtEYˇnr,ω-Y~nr,ω2+EY~nr,ω-Y~n-1r,ω2dr.By the random Romberg algorithm in Section 4.1, we obtain(47)I4≤2K3δ^2K12t+2K3K12∫0nΔtEYˇnr,ω-Y~nr,ω2dr.It follows from the Gronwall inequality that there exists a number M3 such that (48)Yˇnt,ω-Y~nt,ω≤M3,where(49)M3=2K3K12tN+13n2·exp⁡2K3K12tN+1.By the fact that M3 tends to zero as n→+∞, we obtain (50)limn→+∞Yˇnt,ω-Y~nt,ω=0.That is,(51)limn→+∞Δ-Δ¯=0.Therefore, it is mean-square convergent. This proof is finished.


## 4.1. A Detailed Implementation Method of Shadowing Distance

We show the numerical method in detail, which we use for the approximation of shadowing distance, and this method consists of three steps as follows.Step 1.
Utilizing the one-step numerical scheme (Euler-Maruyama (EM) scheme, Milstein scheme [15]) to solve the following equation from tk to tk+1 with the initial values X(0)=X0,(24)dXt=-AXtdt+ft,Xtdt+gtdWt,we obtain the approximations of Xk+1, that is, (ω,δ)-pseudoperiodic orbit of SDE (1), (25)Xk+1=Xk+-AXk+ftk,Xktk+1-tk+gtkΔWk.Step 2.
It follows from Theorem7 that the forward infinite horizon integral equation (15) is the random periodic solution of SDE (1) with new initial conditions which is chosen as Section 3. It follows from Theorem  3.4 in [5] that the random periodic solution (15) is mean-square uniformly asymptotically stable.In order to make this article self-contained we outline the numerical method which is used for approximating the random periodic solution. The reader is referred to the paper [5] for a more detailed description. Therefore, numerical implementation method of the random periodic orbits is described as follows.Firstly, we need to obtain the initial value for the sake of the approximation. It follows from (15) that we get(26)Y0,ω=∫-∞0T-rfr,Yr,ωdr+ω∫-∞0T-rgrdWr.For any given presupposed error tolerance δ^∈(0,Δt], if s′<0 is chosen such that the following inequality holds(27)Y0,ω-∫s′0T-rfr,Yr,ωdr-ω∫s′0T-rgrdWr≤δ^,then the Ito integral Y¯(0,ω) can be used to approximate the improper integral (26), where(28)Y¯0,ω=∫s′0T-rfr,Y¯r,ωdr+ω∫s′0T-rgrdWr,and Δt≔tN+1/N′. Therefore, Y¯(0,ω) can be chosen as the approximating initial value in the given presupposed error tolerance.Secondly, we need to obtain the approximation of the improper integral (15). Therefore the improper integral (15) in the finite-time interval [0,tN+1] can be approximated by the Ito integral (29)(29)Y~tN+1,ω=∫0tN+1Tt-rfr,Y~r,ωdr+ω∫0tN+1Tt-rgrdWr,with initial value Y¯(0,ω) at the time t=0. It follows from (29), pullback theory, and the construction of the random periodic orbits that Y~(tN+1,ω) is ω-wise, too. Here, pullback theory is pullback random analysis theory which is shown as [1] in detail. Furthermore, Y~(tN+1,ω) and Y¯(0,ω) have the same sample ω at the time t=0.Remark 9.
By means of reselecting the corresponding starting time ands′, we can simulate a random periodic solution in an arbitrary finite-time interval with any given presupposed error tolerance.Finally, in order to improve the accuracy of the integral, the random Romberg algorithm is applied to (29) and Y¯(0,ω). The method applied to (29) is shown as follows briefly.LetY~n(t,ω) be the approximation of Y~(0,nΔt,ω); then we can obtain(30)Y~nt,ω=∫0nΔtTnΔt-rfr,Y~n-1r,ωdr+ω∫0nΔtTnΔt-rgrdWr,where n=1,…,N′ and Y~N′(t,ω) is the numerical approximation of Y~(tN+1,ω).For any given presupposed error toleranceδ~∈[0,δ^], if the following inequality holds(31)Rj,j-1-Rjj≤δ~,the computation of the random Romberg algorithm is ended and Rjj is viewed as the approximation of (30). That is,(32)Y~nt,ω=Rjj,where Rj,j-1 and Rjj are obtained by the implementation of the random Romberg algorithm; this algorithm is shown in detail in [5].Step 3.
It follows from Steps1 and 2 and Theorem 7 that we can obtain the approximation of shadowing distance and the Lipschitz constant of Lipschitz shadowing as follows:(33)Δ=maxnXn-Y~nt,ω+δ^,L≔Δδ.


## 4.2. Convergence Analysis

We can divide the time interval[0,tN+1] into N′ subintervals with the length Δt. Then we obtain the exact solution of SDE (1) in [0,tN+1] as follows:(34)Yˇnt,ω=∫0nΔte-AnΔt-rfr,Yˇnr,ωdr+ω∫0nΔte-AnΔt-rgrdWr.Therefore the theoretical shadowing distance is shown as follows:(35)Δ¯=maxnXn-Yˇnt,ω+δ^.We will show that the shadowing distance (33) is bounded. And the numerical approximation (33) to shadowing distance is mean-square convergent to the theoretical result (35).Theorem 10.
Suppose thatX0∈L2(Ω), SDE (1) satisfy the condition of Theorem 7, then the shadowing distance (33) is bounded, and the numerical approximation (33) to shadowing distance is mean-square convergent to the theoretical result (35) by the random Romberg algorithm.Proof.
First and foremost, it follows from (30) that(36)EXn+1-Y~n+1t,ω2≤EI-tn+1-tnAXn-∫0n+1Δtfr,Xn-Tn+1Δt-rfr,Y~nr,ωdr-ω∫0n+1Δt1-Tn+1Δt-rgrdWr2≤I1+I2+I3,where(37)I1=3EI-tn+1-tnAXn2,I2=3E∫0n+1Δtfr,Xn-Tn+1Δt-rfr,Y~nr,ωdr2,I3=3Eω∫0n+1Δt1-Tn+1Δt-rgrdWr2.Utilizing the boundedness of X0, there exists a positive constant M1 such that I1≤M1Δt. By the conclusion, we obtain that 0<T(n+1)Δt-r<1. Then it follows from the integral property and the Lipschitz condition of the function f that(38)I2≤3E∫0n+1Δtfr,Xn-fr,Y~nr,ωdr2≤3K12∫0n+1ΔtEXn-Y~nr,ω2dr.It follows from Ito isometry and the boundness condition of the function g that (39)I3≤3∫0n+1ΔtEgr2dr≤3K22n+1Δt.Therefore by the Gronwall inequality, there exists a number M2 such that (36) implies that(40)Δn=EXn-Y~nt,ω21/2+δ^≤M2,where M2=M1+3K22nΔt·exp⁡3K12nΔt.
By the conclusions (40), the first conclusion of Theorem 10 holds; that is, the shadowing distance Δn is bounded.
Secondly, from the expression ofY~n(t,ω), we obtain(41)Y~nt,ω=∫0nΔte-AnΔt-rfr,Y~n-1r,ωdr+ω∫0nΔte-AnΔt-rgrdWr.Then it implies that(42)EΔ-Δ¯2≤maxn⁡EYˇnt,ω-Y~nt,ω2.We let(43)I4≔EYˇnt,ω-Y~nt,ω2,and we obtain that(44)I4=E∫0nΔte-AnΔt-rfr,Yˇnr,ω-fr,Y~n-1r,ωdr2.It follows from the Cauchy-Schwarz inequality that(45)I4≤∫0nΔte-2AnΔt-rdr·∫0nΔtK12EYˇnr,ω-Y~n-1r,ω2dr≤K3K12∫0nΔtEYˇnr,ω-Y~n-1r,ω2dr,where K3=∫0tN+1e-2A(t-r)dr.
Then the fact that(a+b)2≤2a2+2b2, a,b∈R, implies that(46)I4≤2K3K12∫0nΔtEYˇnr,ω-Y~nr,ω2+EY~nr,ω-Y~n-1r,ω2dr.By the random Romberg algorithm in Section 4.1, we obtain(47)I4≤2K3δ^2K12t+2K3K12∫0nΔtEYˇnr,ω-Y~nr,ω2dr.It follows from the Gronwall inequality that there exists a number M3 such that (48)Yˇnt,ω-Y~nt,ω≤M3,where(49)M3=2K3K12tN+13n2·exp⁡2K3K12tN+1.By the fact that M3 tends to zero as n→+∞, we obtain (50)limn→+∞Yˇnt,ω-Y~nt,ω=0.That is,(51)limn→+∞Δ-Δ¯=0.Therefore, it is mean-square convergent. This proof is finished.


## 5. Numerical Experiments

### 5.1. Experimental Preparation

Assume that we are working in a one-dimensional space of real numbers and consider the following stochastic logistic equation:(52)dXt=-Xtdt+5Xt-4Xt2dt+μsin⁡tdWt,X0=X0;that is,(53)A=1,ft,Xt=5Xt-4Xt2,gt=μsin⁡t.It follows from Theorem7 that there exists random periodic solutions with period τ=2π of SDE (52). If we choose δ^=0.01, s′ is equal to -30 so that the inequality (27) holds. It follows from the results in Section 4.1 that we can obtain the numerical approximations in the presupposed initial error tolerance.As shown in [5], to get the Brownian trajectory for the negative time, we construct the positive time path and reflect it against point zero. We will run the simulation with the following meshes [8, 15]: (54)s′=-30,t=35,Δt=0.1,N+1=350to construct a random periodic solution. We generate Brownian trajectories in the following way: (55)W0=0,Wi+1Δt=WiΔt+ψi+1,where ψi=N0,Δt, i=1,2,…,N+1.First and foremost, utilizing Theorem7 and random Romberg algorithm we obtain the graphs for numerical approximations to random periodic solutions in the time interval [0,35] as Figure 1. As we see there exists random periodic phenomena with period τ=2π with different starting points X0=0.03 and X0=0.06 at the time t=0.Figure 1
Random periodic solutions with the starting pointsX0=0.03 and X0=0.06; μ=2.Secondly, we need to verify the existence of random periodic orbits nearby a numerically computed(ω,δ)-pseudoperiodic orbit of SDE (52).Utilizing the one-step numerical scheme (EM scheme [15]) to solve SDE (52) with the initial value X0=Y0(θt0ω), we obtain(56)Xk+1=Xk+-Xk+5Xk-4Xk2Δtk+μsin⁡tkΔWk,X0=X0,and then we obtain a numerically computed (ω,δ)-pseudoperiodic orbit of SDE (52).Figure2, whose starting points are X0=0.03 and μ=2, also reflects the fact that the true orbit is random periodic, and as we move forward in time, this true random periodic orbit lies in the appropriate neighbourhood of the (ω,δ)-pseudoperiodic orbit of SDE (52). This shows that there exists (ω,Lδ)-periodic Lipschitz shadowing in the systems of SDE (52). Meanwhile, we should pay attention to the fact that the relative position between the (ω,δ)-pseudoperiodic orbit and its (ω,Lδ)-periodic Lipschitz shadowing orbit seems a little far away in some time. And this phenomenon is reflected in Figure 2; that is, the red line and the blue line are sometimes close, and sometimes far away. The reason is that the noise dumped into the systems constantly, and the random periodic solutions are stochastic processes which depend on every ω∈Ω. These results confirm the existence of RPLSO.Figure 2
Existence of RPLSO.


### 5.2. Numerical Results

This section will provide numerical experiments to compute the shadowing distance of SDE (52).Firstly, in order to show the influence of noise on the(ω,δ)-pseudoperiodic orbit, we choose various sizes of noise, such as μ=0 (the deterministic case), μ=0.5, μ=1.5, and μ=2.0. And we take the temporal step size Δt=0.1 and N+1=350. Taking the numerical solution as an example, Figure 3 shows the perturbation of the (ω,δ)-pseudoperiodic orbits of SDE (52) corresponding to differential scales of noise. It shows that the perturbation of the (ω,δ)-pseudoperiodic orbits becomes much more serious both in x and in y directions due to the increase in the scale of the noise when μ>0.Figure 3
Pseudoorbits for different sizes of noise:μ=0, μ=0.5, μ=1.5, and μ=2.0.Secondly, we focus on numerically performing the shadowing distanceε shown as (33). The presupposed error tolerance δ^ is chosen as the step size Δt. The local error δ is approximately determined by the numerical scheme.Table1 presents the numerical results, where δ is the local error and ε is the shadowing distance. It shows the existence of RPLSO and the effectiveness of the numerical method. Because we choose {hk}k=0N+1={tk}k=0N+1, that is, we do not consider the reparameterization of time, the values of shadowing distance ε are not small with respect to the local error δ.Table 1
Summaries of the parameters for stochastic logistic equation.
EM
δ
^
=
0.1
T
=
35.0
δ
^
=
0.05
T
=
17.5
μ
δ
L
δ
δ
L
δ
0.5
0.3162
1.1800
0.2236
1.0148
1.0
0.3162
1.2578
0.2236
1.1579
1.5
0.3162
1.3334
0.2236
1.2097
2.0
0.3162
1.4070
0.2236
1.2600Figure4 presents the shadowing distances of the (ω,δ)-pseudoperiodic orbits by the numerical computation with different error tolerance δ^. The first two figures are about the error tolerance δ^=0.1, and the other two figures are about the error tolerance δ^=0.05. In order to show the detailed case which is shown in Table 1, we only take the iterative step N+1=350. And the curve of the discrete shadowing orbits shows the oscillating property of the (ω,δ)-pseudoperiodic orbits. It also indicates the relevance between the discrete shadowing orbits and the (ω,δ)-pseudoperiodic orbits. This phenomena mean that numerical experiment consists with the theory result of Theorem 7.Figure 4
Zoom in parts showing the results in Table1 for different error tolerance δ^ and noises.As can be seen from these numerical results, there is an explicit dependent relationship between the shadowing distance and the local error, and there exists a true random periodic orbit in the appropriate neighbourhood of a given(ω,δ)-pseudoperiodic orbit of SDE (52). The numerical behavior of the system indeed reflects its real dynamical behavior.Finally, to check the convergence of numerical approximations to the shadowing distance, we plot the curves from different starting points at the timet=0 in the same graph. As we see from Figure 5, whose starting points are X0=0.33 and X0=0.06, respectively, as time progresses, the trajectories become asymptotically close. This also reflects the fact that whatever the starting points we choose, as we move forward in time, the shadowing distance εn arrives at the exact trajectories which depend on different ω∈Ω; that is, the shadowing distance εn is a stochastic process and different for every ω∈Ω, and its upper bound exists. These confirm that the numerical methods are efficient.Figure 5
Convergence of the shadowing distanceεn with different starting points.


## 5.1. Experimental Preparation

Assume that we are working in a one-dimensional space of real numbers and consider the following stochastic logistic equation:(52)dXt=-Xtdt+5Xt-4Xt2dt+μsin⁡tdWt,X0=X0;that is,(53)A=1,ft,Xt=5Xt-4Xt2,gt=μsin⁡t.It follows from Theorem7 that there exists random periodic solutions with period τ=2π of SDE (52). If we choose δ^=0.01, s′ is equal to -30 so that the inequality (27) holds. It follows from the results in Section 4.1 that we can obtain the numerical approximations in the presupposed initial error tolerance.As shown in [5], to get the Brownian trajectory for the negative time, we construct the positive time path and reflect it against point zero. We will run the simulation with the following meshes [8, 15]: (54)s′=-30,t=35,Δt=0.1,N+1=350to construct a random periodic solution. We generate Brownian trajectories in the following way: (55)W0=0,Wi+1Δt=WiΔt+ψi+1,where ψi=N0,Δt, i=1,2,…,N+1.First and foremost, utilizing Theorem7 and random Romberg algorithm we obtain the graphs for numerical approximations to random periodic solutions in the time interval [0,35] as Figure 1. As we see there exists random periodic phenomena with period τ=2π with different starting points X0=0.03 and X0=0.06 at the time t=0.Figure 1
Random periodic solutions with the starting pointsX0=0.03 and X0=0.06; μ=2.Secondly, we need to verify the existence of random periodic orbits nearby a numerically computed(ω,δ)-pseudoperiodic orbit of SDE (52).Utilizing the one-step numerical scheme (EM scheme [15]) to solve SDE (52) with the initial value X0=Y0(θt0ω), we obtain(56)Xk+1=Xk+-Xk+5Xk-4Xk2Δtk+μsin⁡tkΔWk,X0=X0,and then we obtain a numerically computed (ω,δ)-pseudoperiodic orbit of SDE (52).Figure2, whose starting points are X0=0.03 and μ=2, also reflects the fact that the true orbit is random periodic, and as we move forward in time, this true random periodic orbit lies in the appropriate neighbourhood of the (ω,δ)-pseudoperiodic orbit of SDE (52). This shows that there exists (ω,Lδ)-periodic Lipschitz shadowing in the systems of SDE (52). Meanwhile, we should pay attention to the fact that the relative position between the (ω,δ)-pseudoperiodic orbit and its (ω,Lδ)-periodic Lipschitz shadowing orbit seems a little far away in some time. And this phenomenon is reflected in Figure 2; that is, the red line and the blue line are sometimes close, and sometimes far away. The reason is that the noise dumped into the systems constantly, and the random periodic solutions are stochastic processes which depend on every ω∈Ω. These results confirm the existence of RPLSO.Figure 2
Existence of RPLSO.


## 5.2. Numerical Results

This section will provide numerical experiments to compute the shadowing distance of SDE (52).Firstly, in order to show the influence of noise on the(ω,δ)-pseudoperiodic orbit, we choose various sizes of noise, such as μ=0 (the deterministic case), μ=0.5, μ=1.5, and μ=2.0. And we take the temporal step size Δt=0.1 and N+1=350. Taking the numerical solution as an example, Figure 3 shows the perturbation of the (ω,δ)-pseudoperiodic orbits of SDE (52) corresponding to differential scales of noise. It shows that the perturbation of the (ω,δ)-pseudoperiodic orbits becomes much more serious both in x and in y directions due to the increase in the scale of the noise when μ>0.Figure 3
Pseudoorbits for different sizes of noise:μ=0, μ=0.5, μ=1.5, and μ=2.0.Secondly, we focus on numerically performing the shadowing distanceε shown as (33). The presupposed error tolerance δ^ is chosen as the step size Δt. The local error δ is approximately determined by the numerical scheme.Table1 presents the numerical results, where δ is the local error and ε is the shadowing distance. It shows the existence of RPLSO and the effectiveness of the numerical method. Because we choose {hk}k=0N+1={tk}k=0N+1, that is, we do not consider the reparameterization of time, the values of shadowing distance ε are not small with respect to the local error δ.Table 1
Summaries of the parameters for stochastic logistic equation.
EM
δ
^
=
0.1
T
=
35.0
δ
^
=
0.05
T
=
17.5
μ
δ
L
δ
δ
L
δ
0.5
0.3162
1.1800
0.2236
1.0148
1.0
0.3162
1.2578
0.2236
1.1579
1.5
0.3162
1.3334
0.2236
1.2097
2.0
0.3162
1.4070
0.2236
1.2600Figure4 presents the shadowing distances of the (ω,δ)-pseudoperiodic orbits by the numerical computation with different error tolerance δ^. The first two figures are about the error tolerance δ^=0.1, and the other two figures are about the error tolerance δ^=0.05. In order to show the detailed case which is shown in Table 1, we only take the iterative step N+1=350. And the curve of the discrete shadowing orbits shows the oscillating property of the (ω,δ)-pseudoperiodic orbits. It also indicates the relevance between the discrete shadowing orbits and the (ω,δ)-pseudoperiodic orbits. This phenomena mean that numerical experiment consists with the theory result of Theorem 7.Figure 4
Zoom in parts showing the results in Table1 for different error tolerance δ^ and noises.As can be seen from these numerical results, there is an explicit dependent relationship between the shadowing distance and the local error, and there exists a true random periodic orbit in the appropriate neighbourhood of a given(ω,δ)-pseudoperiodic orbit of SDE (52). The numerical behavior of the system indeed reflects its real dynamical behavior.Finally, to check the convergence of numerical approximations to the shadowing distance, we plot the curves from different starting points at the timet=0 in the same graph. As we see from Figure 5, whose starting points are X0=0.33 and X0=0.06, respectively, as time progresses, the trajectories become asymptotically close. This also reflects the fact that whatever the starting points we choose, as we move forward in time, the shadowing distance εn arrives at the exact trajectories which depend on different ω∈Ω; that is, the shadowing distance εn is a stochastic process and different for every ω∈Ω, and its upper bound exists. These confirm that the numerical methods are efficient.Figure 5
Convergence of the shadowing distanceεn with different starting points.


## 6. Conclusion

Finally, conclusions and future work are summarized. In this paper, the main result is(ω,Lδ)-periodic Lipschitz shadowing of a class of stochastic differential equations. The methods shown in this paper focus on the possibility of (ω,Lδ)-periodic Lipschitz shadowing. The results show that the methods are effective and the numerical results are performed and match the results of theoretical analysis. Although some progress is made, more simple and practical methods will be shown in our further work.



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*Source: 1967508-2018-01-11.xml*