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arXiv 0905.1858math.PRmath.DS
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集值动力系统的随机逼近:以正概率收敛于吸引子

Stochastic approximations of set-valued dynamical systems: Convergence with positive probability to an attractor

Mathieu Faure, Gregory Roth

摘要:

描述由某种递推公式控制的离散时间随机过程的渐近行为的一个成功方法是将它与一个精心选择的平均微分方程的极限集联系起来。在可达性条件下,证明了由该动力系统引起的流的收敛到给定吸引子的概率为正(Benaïm,1999)。Benaïm et al.(2005)将这种方法推广到随机逼近算法,其平均行为与微分包含有关。我们追求的类比延伸到这个设置的结果与正概率收敛到吸引子。

英文摘要:

A succesful method to describe the asymptotic behavior of a discrete time stochastic process governed by some recursive formula is to relate it to the limit sets of a well chosen mean differential equation. Under an attainability condition, convergence to a given attractor of the flow induced by this dynamical system was proved to occur with positive probability (Benaïm, 1999) for a class of Robbins Monro algorithms. Benaïm et al. (2005) generalised this approach for stochastic approximation algorithms whose average behavior is related to a differential inclusion instead. We pursue the analogy by extending to this setting the result of convergence with positive probability to an attractor.

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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