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Equivalence of the mean square stability between the partially truncated Euler–Maruyama method and stochastic differential equations with super-linear growing coefficients
Advances in Difference Equations volume 2018, Article number: 355 (2018)
Abstract
For stochastic differential equations (SDEs) whose drift and diffusion coefficients can grow super-linearly, the equivalence of the asymptotic mean square stability between the underlying SDEs and the partially truncated Euler–Maruyama method is studied. Using the finite time convergence as a bridge, a twofold result is proved. More precisely, the mean square stability of the SDEs implies that of the partially truncated Euler–Maruyama method, and the mean square stability of the partially truncated Euler–Maruyama method indicates that of the SDEs given the step size is carefully chosen.
1 Introduction
In [9], the authors initialed the study on the equivalence of the stability between the underlying equations and their numerical methods for stochastic differential equations (SDEs). More precisely, for the SDE of the Itô type
with the initial value \(y(0) = y_{0}\) satisfying \(\mathbb {E}|y_{0}|^{2} < \infty\). One says that the solution is mean square stable if there exist positive constants \(K_{s}\) and \(\lambda_{s}\) such that
Denote some numerical approximations to \(y(t)\) by \(x_{\Delta}(t)\) with \(x_{\Delta}(0) = y(0)\). One claims that the numerical solution is mean square stable if there exist positive constants \(K_{n}\) and \(\lambda_{n}\) such that
The authors in [9] proved a very general “if and only if” theorem between (1.1) and (1.2). The theorem states in twofold that
-
(A)
the mean square stability of the SDE implies the mean square stability of the numerical method,
-
(B)
the mean square stability of the numerical method implies the mean square stability of the SDE if the step size Δ of the numerical method is carefully chosen.
By saying the theorem is very general, one means that conditions required are the second moment boundedness of the numerical method and the finite time convergence of the numerical method to the SDE (see the details in Sect. 3), where the structure of the numerical method is not needed to be specified.
Since then, many works have been devoted to the study on the equivalence of the mean square stability between different types of SDEs and their numerical methods. The author in [18] investigated the stochastic differential delay equations (SDDEs) and the Euler–Maruyama (EM) method. The SDDEs with Poisson jump and Markov switching and the semi-implicit Euler method are studied in [28]. The authors in [17] analyze the neutral delayed stochastic differential equations and the EM method.
It should be pointed out that although the very general results are obtained in the papers above for different types of SDEs and numerical methods, the global Lipschitz condition is always imposed on the drift and diffusion coefficients when the theorems are applied to some specified numerical methods. One may notice that SDEs with the coefficients not obeying the global Lipschitz condition have been extensively applied more recently in many areas such as biology, finance, and epidemiology [1, 4, 19, 23]. Therefore, the first motivation of this paper is as follows.
-
(M1)
For some SDEs whose coefficients do not satisfy the global Lipschitz condition, can we find some numerical method that shares the mean square stability with the underlying SDEs, i.e., both (A) and (B) hold?
Actually, many interesting works have been devoted to (A), i.e., given the SDE is stable under certain conditions, some numerical method can reproduce such a stability. We just mention some of the works here [2, 3, 7, 10, 11, 14, 22, 26, 27] and refer the readers to the references therein. It is not hard to observe that when the coefficients of SDE do not satisfy the global Lipschitz condition, the classical EM method is always abandoned and some implicit methods are adapted as alternatives. This phenomenon is explained in [12], where the authors proved that the classical EM diverges from the SDE if either drift or diffusion coefficient grows super-linearly.
However, due to the advantages such as simple structure and less computational cost in each iteration (not like implicit methods in which some non-linear equation system needs to be solved in each iteration) [8], the explicit Euler-type methods are still attracting lots of attention. Therefore, in the past several years some modified explicit Euler methods, such as the tamed Euler method [13, 24, 25, 29] and the truncated EM method [6, 15, 16, 20, 21], have been developed. The bloom of explicit methods brings the second motivation of this paper as follows.
-
(M2)
Can we use some explicit method to answer (M1), i.e., can we find some explicit methods for some SDEs with super-linear growing coefficients that shares the mean square stability with the underlying SDEs?
Bearing (M1) and (M2) in mind, in this paper we investigate the partially truncated EM method to see if it could share the mean square stability with the SDEs when both the drift and diffusion coefficients can grow super-linear. By using the finite time convergence as the bridge, we prove the “if and only if” theorem for the partially truncated EM method. More precisely, we prove that
-
(a)
the mean square stability of the SDE implies the mean square stability of the partially truncated EM method,
-
(b)
the mean square stability of the partially truncated EM method implies the mean square stability of the SDE if the step size Δ of the numerical method is carefully chosen.
To our best knowledge, few works have dealt with (B) using explicit methods for SDEs with super-linear coefficients, though some works have studied (A) using explicit methods [6, 29]. As pointed out in [9], sometimes (B) is more interesting and important since if (B) is true for some numerical method, then by carefully conducting the numerical simulation one can know whether the SDE is stable or not without using the Lyapunov technique.
This paper is constructed as follows. In Sect. 2, a general theorem that guarantees the equivalence of the stability between the SDEs and their numerical methods is provided, which is a slight generalization of Theorem 2.6 in [9]. Our main results for the partially truncated EM method are presented in Sect. 3. Section 4 concludes this paper with some future research mentioned.
2 A general theorem
Throughout this paper, unless otherwise specified, the following notations are used. The transpose of a vector or matrix A is denoted by \(A^{T}\). \(|y|\) is the Euclidean norm if \(y\in \mathbb {R}^{d}\). If A is a matrix, we let \(|A| = \sqrt{ \operatorname{trace}(A^{T}A)}\) be its trace norm. For two real numbers α and β, we use \(\alpha \vee \beta=\max(\alpha , \beta)\) and \(\alpha \wedge \beta= \min(\alpha, \beta)\). If D is a set, its indicator function is denoted by \(I_{D}\), namely \(I_{D}(x)=1\) if \(x\in D\) and 0 otherwise. Moreover, let \((\Omega, \mathcal{F}, \mathbb {P})\) be a complete probability space with a filtration \(\{\mathcal{F}_{t} \}_{t \ge0}\) satisfying the usual conditions (that is, it is right continuous and increasing while \(\mathcal{F}_{0}\) contains all \(\mathbb {P}\)-null sets), and let \(\mathbb {E}\) denote the expectation corresponding to \(\mathbb {P}\). Let \(B(t)\) be an m-dimensional Brownian motion defined on the space.
In this paper, we study stochastic differential equations of the Itô type
with the initial value \(y(0) = y_{0}\), where
Theorem 2.1
Assume that, for all sufficiently small step size Δ, a numerical method applied to (2.1) with initial value \(x_{\Delta}(0) = y(0)=y_{0}\) satisfies:
-
(I)
for any \(T > 0\),
$$ \sup_{0 \leq t \leq T} \mathbb {E}\bigl\vert x_{\Delta}(t) \bigr\vert ^{2} < C(y_{0},T), $$where \(C(y_{0},T)\) depends on \(y_{0}\) and T, but not on Δ;
-
(II)
there exists a strictly increasing continuous function \(\alpha(\Delta)\) with \(\alpha(0) = 0\) such that
$$ \sup_{0 \leq t \leq T} \mathbb {E}\bigl\vert x_{\Delta}(t) - y(t) \bigr\vert ^{2} \leq \Bigl( \sup_{0 \leq t \leq T} \mathbb {E}\bigl\vert x_{\Delta}(t) \bigr\vert ^{2} \Bigr) C_{T} \alpha( \Delta), $$where \(C_{T}\) depends on T but not on \(y_{0}\) and Δ.
Then the SDE is mean square exponentially stable if and only if there exists \(\Delta> 0\) such that the numerical method is mean square exponentially stable with rate constant \(\lambda_{n}\), growth constant \(K_{n}\), step size Δ, and the global error constant \(C_{T}\) with \(T:= 1 + (4 \log K_{n})/\lambda_{n}\) satisfying
Remark 2.2
Theorem 2.1 is a general theorem for any numerical method. Compared with Condition 2.3 for Theorem 2.6 in [9], one may notice that a more general function of Δ, i.e., \(\alpha (\Delta)\), is used in Theorem 2.1. This change enables the new theorem to cover numerical methods with different convergence rates. For example, in this paper the partially truncated EM method needs \(\alpha(\Delta) = \Delta^{\epsilon}\) with \(\epsilon\in (0,1)\), which is not covered by Theorem 2.6 in [9].
Remark 2.3
Although Theorem 2.1, to some extent, could be regarded as a generalization of Theorem 2.6 in [9], the proof follows a similar manner. Therefore, we refer the readers to [9] for the detailed proof. In this paper, we focus on how to fulfill (I) and (II) by using the partially truncated EM method for SDEs with super-linear growing coefficients.
3 Main results
We start this section by imposing some conditions on the coefficient and introducing the partially truncated EM method in Sect. 3.1. The main results and proofs are presented in Sect. 3.2
3.1 Partially truncated EM method
We assume that both the drift and diffusion coefficients in (2.1) could be separated into two parts as follows:
We impose some assumptions on \(\mu_{i}\) and \(\sigma_{i}\) for \(i=1,2\).
Assumption 3.1
Assume that there exist constants \(L_{1} \geq1\) and \(\gamma\geq0\) such that, for any \(x,y \in \mathbb {R}^{d}\),
and
Assumption 3.2
Assume that there is a pair of constants \(\bar{r} > 2\) and \(L_{2}\) such that
for all \(x,y\in \mathbb {R}^{d}\).
For the purpose of the study on the stability, we require
Assumption 3.3
Assume that there are constants \(\bar{p} >\bar{r}\) and \(K_{2}>0\) such that
for all \(x\in \mathbb {R}^{d}\).
As pointed out in [6], Assumption 3.3 cannot be deduced from Assumption 3.2 as we need \(\bar {p} >\bar{r}\).
In addition, it can be derived from Assumption 3.1 that \(\mu_{1}\) and \(\sigma_{1}\) satisfy the linear growth condition and \(\mu _{2}\) and \(\sigma_{2}\) satisfy the polynomial growth condition that there exists a constant \(K_{1}>0\) such that, for all \(x\in \mathbb {R}^{d}\),
and
It is not hard to see that the combination of Assumption 3.3 and (3.1) can imply that, for any \(p \in(2,\bar{p})\),
for all \(x\in \mathbb {R}^{d}\), where \(K_{3}\) is a constant dependent on \(K_{1}\), \(K_{2}\), p, and p̄.
It can also be seen that under Assumptions 3.1 and 3.2, for any \(q \in(2,\bar{r})\),
where \(L_{3}\) is a constant dependent on \(L_{1}\), \(L_{2}\), q, and r̄.
To make the paper self-contained, we provide the definition of the partially truncated EM method here and refer for the original ideas to [5, 6]. Firstly, we choose a strictly increasing continuous function \(\kappa: \mathbb {R}_{+} \rightarrow \mathbb {R}_{+}\) satisfying
It is clear to see that the inverse function of κ, denoted by \(\kappa^{-1}\), is strictly increasing continuous from \([\kappa (0),\infty)\) to \(\mathbb {R}_{+}\).
Next, we choose a strictly decreasing continuous function \(h: (0,1] \rightarrow(0,\infty)\) such that
Remark 3.4
Comparing (3.5) with (2.9) in [5], we need \(\Delta^{1/4}h(\Delta)\) to be bounded by the initial value here. It should be noted that this requirement is not hard to satisfy as the initial value \(y_{0}\) is always provided. In addition, such an upper bound is key in the proofs of our results.
For a given time step \(\Delta\in(0,1] \), we define the truncating function \(\pi_{\Delta}: \mathbb {R}_{d} \rightarrow \mathbb {R}_{d}\) by
Now, we embed the truncating function into \(\mu_{2}\) and \(\sigma_{2}\) to get
It is easy to see that, for any \(x\in \mathbb {R}^{d}\),
Using the notations that
the partially truncated EM method is defined as
where \(\Delta B_{i} = B((i+1)\Delta) - B(i\Delta)\) is the Brownian motion increment for \(i = 1,2,3,\dots\), and \(x_{\Delta,i}\) is the numerical approximation to \(y(i\Delta)\) for \(i = 1,2,3,\dots\).
In some cases, it is more convenient to work with the continuous version of the numerical method. Thus, we define the continuous version of (3.7) by
where
3.2 (I) and (II) for the partially truncated EM method
To show that the SDE is mean square exponentially stable if and only if the partially truncated EM method is mean square exponentially stable, we need to prove that (I) and (II) in Theorem 2.1 hold for the method.
One may argue that the second moment boundedness and the \(L^{2}\) strong convergence, i.e., requirements (I) and (II) in Theorem 2.1, have already been proven in [5, 6]. But it should be noted that (II) requires the \(L^{2}\) strong error to be linearly related to \(\sup_{0 \leq t \leq T} \mathbb {E}|x_{\Delta}(t)|^{2}\) and this special structure of the error is important to the proof of Theorem 2.1. By looking into [5, 6], one notices that no such structure of error is provided.
Therefore, the main task of this part is to prove (II) for the partially truncated EM method by carefully tracking the related constants. We need several lemmas before we proceed to the main theorem.
Firstly, we cite two lemmas from [5]. Roughly speaking, Lemma 3.5 shows that \(\mu_{2,\Delta}\) and \(\sigma _{2,\Delta}\) inherit Assumption 3.3. And Lemma 3.6 shows that \(\mu_{\Delta}\) and \(\sigma_{\Delta}\) inherit Assumption 3.1.
Lemma 3.5
Let Assumption 3.3 hold. Then, for all \(\Delta \in(0,1]\), we have
where \(K_{4} = 2K_{2} (1 \vee [1/\kappa^{-1}(h(1))] )\).
Lemma 3.6
Let Assumption 3.1 hold. Then
for all \(x,y\in \mathbb {R}^{d}\). Moreover, for any \(\Delta \in(0,1]\),
for all \(x,y\in \mathbb {R}^{d}\).
Lemma 3.7
Suppose that (3.5) and Assumptions 3.1, 3.2, and 3.3 hold, then the partially truncated EM method solution (3.8) satisfies
where
Proof
From (3.8) and by the Itô formula, we have
By (3.9), we have
where
and
By the Hölder inequality and the Young inequality \(\alpha^{b} \beta ^{1-b} \leq b \alpha+ (1-b) \beta\) for \(\alpha, \beta\geq0 \) and \(b \in(0,1)\), we have
Then we obtain the estimate of \(J_{1}\)
In a similar manner, by (3.1) we have
Thus, we have the estimate for \(J_{2}\)
Now we are going to estimate \(J_{3}\). By the Hölder inequality, the Young inequality, and (3.6), we have
By the Hölder inequality, the elementary inequality, (3.1), (3.6), Theorem 7.1 on page 39 of [19], for \(s \in[i\Delta, (i+1)\Delta)\), we have
Combining the two inequalities above and using the fact that \(|\bar {x}_{\Delta}(s)|^{p/2} \leq1 + |\bar{x}_{\Delta}(s)|^{p}\), we have the estimate for \(J_{3}\)
where (3.5) is used. Putting the estimates for \(J_{1}\), \(J_{2}\), and \(J_{3}\) together, we have that
where \(\Delta< 1\) is used. Since \(\sup_{0 \leq u \leq s}\mathbb {E}|x_{\Delta}(u)|^{p} = \sup_{0 \leq u \leq s} \mathbb {E}|\bar{x}_{\Delta}(u)|^{p}\) and the inequality above holds for any \(t \in[0,T]\) and any \(\Delta\in (0,1]\), we obtain that
where
□
The next lemma can be proved by following the typical way, we refer the readers to, for example, [19] for details.
Lemma 3.8
Suppose that (3.3) holds, the solution to (2.1) satisfies
where \(C_{2} = y_{0} \exp (pK_{3}T )\).
Lemma 3.9
Suppose that Assumption 3.1 holds, then
where
Proof
We see from (3.8) that, for any \(t \in[i\Delta ,(i+1)\Delta)\),
where the Hölder inequality, Theorem 7.1 on page 39 of [19] and the elementary inequality \(|a+b|^{4} \leq8 (|a|^{4} + |b|^{4})\) have been used.
Now, using (3.1) and (3.6), we have
where Lemma 3.7 is used for the last inequality.
From (3.5), we derive \(\Delta(h(\Delta))^{4} \leq|y_{0}|^{4}\) and \(\Delta\leq|y_{0}|^{4}\). Then
Taking square root on both sides, we have
where the fact \(|y_{0}|^{2} = |x_{\Delta}(0)|^{2} \leq\sup_{0 \leq t \leq T} \mathbb {E}\vert x_{\Delta}(t) \vert ^{2}\) is used. Since the analysis above holds for any \(t \in[0,T]\), the assertion is obtained. □
For any real number \(R > |y_{0}|\), define the stopping times
Set
Lemma 3.10
Suppose that Assumptions 3.1, 3.2, and 3.3 hold, then for any \(t \in[0,T]\)
where
Proof
For any \(t \in[0,T]\), it is clear that \(t \wedge\theta_{R} \leq\theta _{R}\) a.s. Therefore, we observe from the definitions of \(\mu_{\Delta}\) and \(\sigma_{\Delta}\) that \(\mu_{\Delta}(\bar{x}_{\Delta}(t)) = \mu (\bar{x}_{\Delta}(t))\) and \(\sigma_{\Delta}(\bar{x}_{\Delta}(t)) = \sigma(x_{\Delta}(t))\).
By the Itô formula, we have
Now, using the elementary inequality, we can see that
where \(q >2\) is used. Thus we obtain
where
and
Applying (3.4) yields
Applying the elementary inequality and (3.10) gives
where \(t \wedge\theta_{R} \leq T\) a.s. for \(t \in[0,T]\) is used for the last integral. Using Lemmas 3.7 and 3.9, we have
where the elementary inequality \(|a + b + c|^{4} \leq3^{3} (|a|^{4} + |b|^{4} + |c|^{4})\) is used.
Combining the estimates of \(J_{4}\) and \(J_{5}\) and applying the Gronwall inequality prove the assertion. □
Theorem 3.11
Suppose that Assumptions 3.1, 3.2, and 3.3 hold, then for any \(t \in[0,T]\)
where
Proof
For any \(t \in[0,T]\), we have
For any \(\delta> 0\), the Young inequality yields
Due to Lemmas 3.7 and 3.8, we obtain
By the definition of \(\tau_{R}\), it is not hard to see
In a similar way, we have
Thus, we see that
Now choosing \(\delta= |y_{0}|^{2} \Delta^{1/2}\) and \(R = ( |y_{0}|^{2} \Delta^{1/2} )^{-1/(p-2)}\) gives
where the fact \(|y_{0}|^{2} = |x_{\Delta}(0)|^{2} \leq ( \sup_{0 \leq t \leq T} \mathbb {E}|x_{\Delta}(t)|^{2} )\) is used. Using Lemma 3.10, we have
Hence, the proof is completed. □
We finish this section by providing the theorem of equivalence for the partially truncated Euler–Maruyama method. The proof of the theorem is straightforward following Theorem 2.1, Lemma 3.7, and Theorem 3.11.
Theorem 3.12
Suppose that Assumptions 3.1, 3.2, and 3.3 hold, then the SDE is mean square exponentially stable if and only if the partially truncated Euler–Maruyama method solution is mean square exponentially stable providing the step size is small enough and satisfies (3.5).
4 Conclusion and future research
For stochastic differential equations with super-linear growing coefficients, this paper studies equivalence of the mean square stability between the partially truncated Euler–Maruyama method and the underlying SDEs. By carefully tracking the constant term in the finite time convergence error, the “if and only if” result is obtained.
For the equivalence of other types of stabilities, such as pth moment stability or almost sure stability, we will report in the future works.
References
Allen, E.: Modeling with Itô Stochastic Differential Equations. Mathematical Modelling: Theory and Applications, vol. 22. Springer, Dordrecht (2007)
Appleby, J.A.D., Berkolaiko, G., Rodkina, A.: Non-exponential stability and decay rates in nonlinear stochastic difference equations with unbounded noise. Stochastics 81(2), 99–127 (2009)
Buckwar, E., Kelly, C.: Towards a systematic linear stability analysis of numerical methods for systems of stochastic differential equations. SIAM J. Numer. Anal. 48(1), 298–321 (2010)
Gray, A., Greenhalgh, D., Hu, L., Mao, X., Pan, J.: A stochastic differential equation SIS epidemic model. SIAM J. Appl. Math. 71(3), 876–902 (2011)
Guo, Q., Liu, W., Mao, X.: A note on the partially truncated Euler–Maruyama method. Appl. Numer. Math. 130, 157–170 (2018)
Guo, Q., Liu, W., Mao, X., Yue, R.: The partially truncated Euler–Maruyama method and its stability and boundedness. Appl. Numer. Math. 115, 235–251 (2017)
Higham, D.J.: Mean-square and asymptotic stability of the stochastic theta method. SIAM J. Numer. Anal. 38(3), 753–769 (2000)
Higham, D.J.: Stochastic ordinary differential equations in applied and computational mathematics. IMA J. Appl. Math. 76(3), 449–474 (2011)
Higham, D.J., Mao, X., Stuart, A.M.: Exponential mean-square stability of numerical solutions to stochastic differential equations. LMS J. Comput. Math. 6, 297–313 (2003)
Higham, D.J., Mao, X., Yuan, C.: Almost sure and moment exponential stability in the numerical simulation of stochastic differential equations. SIAM J. Numer. Anal. 45(2), 592–609 (2007)
Hu, Y., Wu, F., Huang, C.: Stochastic stability of a class of unbounded delay neutral stochastic differential equations with general decay rate. Int. J. Syst. Sci. 43(2), 308–318 (2012)
Hutzenthaler, M., Jentzen, A., Kloeden, P.E.: Strong and weak divergence in finite time of Euler’s method for stochastic differential equations with non-globally Lipschitz continuous coefficients. Proc. R. Soc. Lond., Ser. A, Math. Phys. Eng. Sci. 467(2130), 1563–1576 (2011)
Hutzenthaler, M., Jentzen, A., Kloeden, P.E.: Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients. Ann. Appl. Probab. 22(4), 1611–1641 (2012)
Lamba, H., Mattingly, J.C., Stuart, A.M.: An adaptive Euler–Maruyama scheme for SDEs: convergence and stability. IMA J. Numer. Anal. 27(3), 479–506 (2007)
Lan, G., Xia, F.: Strong convergence rates of modified truncated EM method for stochastic differential equations. J. Comput. Appl. Math. 334, 1–17 (2018)
Li, X., Mao, X., Yin, G.: Explicit numerical approximations for stochastic differential equations in finite and infinite horizons: truncation methods, convergence in pth moment, and stability. IMA J. Numer. Anal. (2018). https://doi.org/10.1093/imanum/dry015
Liu, L., Li, M., Deng, F.: Stability equivalence between the neutral delayed stochastic differential equations and the Euler–Maruyama numerical scheme. Appl. Numer. Math. 127, 370–386 (2018)
Mao, X.: Exponential stability of equidistant Euler–Maruyama approximations of stochastic differential delay equations. J. Comput. Appl. Math. 200(1), 297–316 (2007)
Mao, X.: Stochastic Differential Equations and Applications, 2nd edn. Horwood, Chichester (2008)
Mao, X.: The truncated Euler–Maruyama method for stochastic differential equations. J. Comput. Appl. Math. 290, 370–384 (2015)
Mao, X.: Convergence rates of the truncated Euler–Maruyama method for stochastic differential equations. J. Comput. Appl. Math. 296, 362–375 (2016)
Mitsui, T., Saito, Y.: MS-stability analysis for numerical solutions of stochastic differential equations—beyond single-step single dim. In: Some Topics in Industrial and Applied Mathematics. Ser. Contemp. Appl. Math. CAM, vol. 8, pp. 181–194. Higher Education Press, Beijing (2007)
Platen, E., Bruti-Liberati, N.: Numerical Solution of Stochastic Differential Equations with Jumps in Finance. Stochastic Modelling and Applied Probability, vol. 64. Springer, Berlin (2010)
Sabanis, S.: A note on tamed Euler approximations. Electron. Commun. Probab. 18, Article ID 47 (2013)
Sabanis, S.: Euler approximations with varying coefficients: the case of superlinearly growing diffusion coefficients. Ann. Appl. Probab. 26(4), 2083–2105 (2016)
Schurz, H.: Stability, Stationarity, and Boundedness of Some Implicit Numerical Methods for Stochastic Differential Equations and Applications. Logos Verlag Berlin, Berlin (1997)
Wu, F., Mao, X., Szpruch, L.: Almost sure exponential stability of numerical solutions for stochastic delay differential equations. Numer. Math. 115(4), 681–697 (2010)
Zhao, G., Song, M., Liu, M.: Numerical solutions of stochastic differential delay equations with jumps. Int. J. Numer. Anal. Model. 6(4), 659–679 (2009)
Zong, X., Wu, F., Huang, C.: Convergence and stability of the semi-tamed Euler scheme for stochastic differential equations with non-Lipschitz continuous coefficients. Appl. Math. Comput. 228, 240–250 (2014)
Funding
The authors would like to thank the National Natural Science Foundation of China (11701378, 11871343), “Chenguang Program” supported by both Shanghai Education Development Foundation and Shanghai Municipal Education Commission (16CG50) and Shanghai Pujiang Program (16PJ1408000) for their financial support.
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Jiang, Y., Huang, Z. & Liu, W. Equivalence of the mean square stability between the partially truncated Euler–Maruyama method and stochastic differential equations with super-linear growing coefficients. Adv Differ Equ 2018, 355 (2018). https://doi.org/10.1186/s13662-018-1818-1
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DOI: https://doi.org/10.1186/s13662-018-1818-1