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arXiv 1206.7063math.PR
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用惩罚方法求反射扩散的弱近似和强近似

Weak and strong approximations of reflected diffusions via penalization methods

Leszek Slominski

摘要:

研究了$\Rd$的凸子集$D$上的反射伊藤扩散问题,并利用带惩罚项的随机微分方程的解来逼近.我们假设扩散系数仅仅是可测量的(可能是不连续的)函数。在Lipschitz连续系数的情况下,我们给出了对每个$p\geq1$的$L^p$逼近率.我们证明了如果$D$是凸多面体,则速率是$O((\frac{\ln n}n)^{1/2})$,并且在一般情况下速率是$O((\frac{\ln n}n)^{1/4})$。

英文摘要:

We study approximations of reflected Itô diffusions on convex subsets $D$ of $\Rd$ by solutions of stochastic differential equations with penalization terms. We assume that the diffusion coefficients are merely measurable (possibly discontinuous) functions. In the case of Lipschitz continuous coefficients we give the rate of $L^p$ approximation for every $p\geq1$. We prove that if $D$ is a convex polyhedron then the rate is $O((\frac{\ln n}n)^{1/2})$, and in the general case the rate is $O((\frac{\ln n}n)^{1/4})$.

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