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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.02153 |
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| _version_ | 1866929484953812992 |
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| author | Wang, Jian Yang, Hao |
| author_facet | Wang, Jian Yang, Hao |
| contents | In this paper, we provide a criterion on uniform large deviation principles (ULDP) for stochastic differential equations under locally weak monotone conditions and Lyapunov conditions, which can be applied to stochastic systems with coefficients of polynomial growth and possible degenerate driving noises, including the stochastic Hamiltonian systems. The weak convergence method plays an important role in obtaining the ULDP. This result extends the scope of applications of the main theorem in \cite{WYZZ}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02153 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uniform large deviation principles for SDEs under locally weak monotonicity conditions Wang, Jian Yang, Hao Probability In this paper, we provide a criterion on uniform large deviation principles (ULDP) for stochastic differential equations under locally weak monotone conditions and Lyapunov conditions, which can be applied to stochastic systems with coefficients of polynomial growth and possible degenerate driving noises, including the stochastic Hamiltonian systems. The weak convergence method plays an important role in obtaining the ULDP. This result extends the scope of applications of the main theorem in \cite{WYZZ}. |
| title | Uniform large deviation principles for SDEs under locally weak monotonicity conditions |
| topic | Probability |
| url | https://arxiv.org/abs/2409.02153 |