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arXiv 1706.04237math.NAcs.NA
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Langevin动力学模型的算子分裂方法的strong convergence

The strong convergence of operator-splitting methods for the Langevin dynamics model

Adam Telatovich, Xiantao Li

摘要:

研究了具有加性噪声的Langevin动力学模型的几种算子分裂方法的强收敛性。它将表明,直接分裂的确定性和随机项,包括对称分裂方法,只提供强收敛的顺序1。为了提高强收敛阶,提出了一类新的基于Kunita解表示的算子分裂方法。我们目前的随机算法与强订单高达3。数学分析和数值证据提供验证所需的精度顺序。

英文摘要:

We study the strong convergence of some operator-splitting methods for the Langevin dynamics model with additive noise. It will be shown that a direct splitting of deterministic and random terms, including the symmetric splitting methods, only offers strong convergence of order 1. To improve the order of strong convergence, a new class of operator-splitting methods based on Kunita's solution representation are proposed. We present stochastic algorithms with strong orders up to 3. Both mathematical analysis and numerical evidence are provided to verify the desired order of accuracy.

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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