Asymptotic error distribution for stochastic Runge--Kutta methods of strong order one
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912517974917120 |
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| author | Jin, Diancong |
| author_facet | Jin, Diancong |
| contents | This work gives the asymptotic error distribution of the stochastic Runge--Kutta (SRK) method of strong order $1$ applied to Stratonovich-type stochastic differential equations. For dealing with the implicitness introduced in the diffusion term, we propose a framework to derive the asymptotic error distribution of diffusion-implicit or fully implicit numerical methods, which enables us to construct a fully explicit numerical method sharing the same asymptotic error distribution as the SRK method. Further, we show that the limit distribution $U(T)$ satisfies $\mathbf E|U(T)|^2\le e^{L_1T}(1+η_1)T^3$ for some $η_1\ge0$ only depending on the coefficients of the SRK method. Thus, we infer that $η_1$ is the key parameter reflecting the growth rate of the mean-square error of the SRK method. Especially, among the SRK methods of strong order $1$, those of weak order $2$ correspond to $η_1=0$, sharing the unified asymptotic error distribution, and have the smallest mean-square errors after a long time. This property is also found for the case of additive noise. It seems that we are the first to give the asymptotic error distribution of fully implicit numerical methods for stochastic differential equations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Asymptotic error distribution for stochastic Runge--Kutta methods of strong order one Jin, Diancong Numerical Analysis Probability This work gives the asymptotic error distribution of the stochastic Runge--Kutta (SRK) method of strong order $1$ applied to Stratonovich-type stochastic differential equations. For dealing with the implicitness introduced in the diffusion term, we propose a framework to derive the asymptotic error distribution of diffusion-implicit or fully implicit numerical methods, which enables us to construct a fully explicit numerical method sharing the same asymptotic error distribution as the SRK method. Further, we show that the limit distribution $U(T)$ satisfies $\mathbf E|U(T)|^2\le e^{L_1T}(1+η_1)T^3$ for some $η_1\ge0$ only depending on the coefficients of the SRK method. Thus, we infer that $η_1$ is the key parameter reflecting the growth rate of the mean-square error of the SRK method. Especially, among the SRK methods of strong order $1$, those of weak order $2$ correspond to $η_1=0$, sharing the unified asymptotic error distribution, and have the smallest mean-square errors after a long time. This property is also found for the case of additive noise. It seems that we are the first to give the asymptotic error distribution of fully implicit numerical methods for stochastic differential equations. |
| title | Asymptotic error distribution for stochastic Runge--Kutta methods of strong order one |
| topic | Numerical Analysis Probability |
| url | https://arxiv.org/abs/2506.08937 |