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arXiv 2409.02153math.PR
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局部弱单调性条件下随机微分方程的一致大偏差原理

Uniform large deviation principles for SDEs under locally weak monotonicity conditions

Jian Wang, Hao Yang

AI总结:

本文在局部弱单调与Lyapunov条件下,利用弱收敛方法建立了随机微分方程的一致大偏差原理,并将其推广到多项式增长系数、退化噪声及随机Hamilton系统。

摘要:

本文针对随机微分方程,在局部弱单调条件和Lyapunov条件下,给出了一个一致大偏差原理(ULDP)的判定准则。该准则可应用于具有多项式增长系数和可能退化驱动噪声的随机系统,包括随机Hamilton系统。弱收敛方法在获得ULDP的过程中发挥了重要作用。该结果扩展了\cite{WYZZ}中主要定理的应用范围。

英文摘要:

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}.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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