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The ODE method and spectral theory of Markov operators

机译:马尔可夫运营商的颂歌方法和光谱理论

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We give a development of the ODE method for the analysis of recursive algorithms described by a stochastic recursion. With variability modeled via an underlying Markov process, and under general assumptions, the following results are obtained: (i) Stability of an associated ODE implies that the stochastic recursion is stable in a strong sense when a gain parameter is small. (ii) The range of gain-values is quantified through a spectral analysis of an associated linear operator, providing a non-local theory, even for nonlinear systems (iii) A second-order analysis shows precisely how variability leads to sensitivity of the algorithm with respect to the gain parameter. All results are obtained within the natural operator-theoretic framework of geometrically ergodic Markov processes.
机译:我们展开了用于分析随机递归描述的递归算法的ode方法。利用通过底层马尔可夫过程建模的可变性,并且在一般假设下,获得以下结果:(i)相关ode的稳定性意味着当增益参数小时,随机递归在强烈的感觉中是稳定的。 (ii)(ii)通过相关联的线性操作员的光谱分析量化增益值范围,提供非局部理论,即使对于非线性系统(III),即使是非线性系统(III),二阶分析表明是如何变化导致算法的灵敏度关于增益参数。所有结果都在几何麦片Markov方法的自然操作员 - 理论框架内获得。

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