Convergence of an operator splitting scheme for abstract stochastic evolution equations
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arXiv
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866913619428507648 |
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| author | Padgett, Joshua L Sheng, Qin |
| author_facet | Padgett, Joshua L Sheng, Qin |
| contents | In this paper we study the convergence of a Lie-Trotter operator splitting for stochastic semi-linear evolution equations in a Hilbert space. The abstract Hilbert space setting allows for the consideration of convergence of the approximation for both the original and spatially discretized problems. It is known that the strong convergence of this scheme is classically of half-order, at best. We demonstrate that this is in fact the optimal order of convergence in the proposed setting, with the actual order being dependent upon the regularity of noise collected from applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_06371 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Convergence of an operator splitting scheme for abstract stochastic evolution equations Padgett, Joshua L Sheng, Qin Numerical Analysis In this paper we study the convergence of a Lie-Trotter operator splitting for stochastic semi-linear evolution equations in a Hilbert space. The abstract Hilbert space setting allows for the consideration of convergence of the approximation for both the original and spatially discretized problems. It is known that the strong convergence of this scheme is classically of half-order, at best. We demonstrate that this is in fact the optimal order of convergence in the proposed setting, with the actual order being dependent upon the regularity of noise collected from applications. |
| title | Convergence of an operator splitting scheme for abstract stochastic evolution equations |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/1901.06371 |