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arXiv 1301.7740math.PR
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随机逼近格式的输运熵不等式和偏差估计

Transport-entropy inequalities and deviation estimates for stochastic approximation schemes

Max Fathi, Noufel Frikha

摘要:

我们得到了新的输运熵不等式,作为副产品,新的偏差估计的两种离散随机逼近计划的法律。第一个是指在一个固定的确定性的日期和第二个涉及的法律的随机逼近算法在一个给定的时间步长的扩散过程的欧拉样离散计划的法律。我们的结果显着改善和完善[Frikha,Menozzi,2012]中获得的结果。关键是要适当地量化的扩散项的浓度制度的贡献。我们还得到了一个一般的非渐近偏差界的连续欧拉计划的轨迹与扩散过程和它的平均值之间的差异的功能。最后,我们得到了轨迹平均的随机逼近的非渐近界,特别是我们证明了,平均一个随机逼近算法与一个缓慢下降的步骤序列产生的最佳浓度率。

英文摘要:

We obtain new transport-entropy inequalities and, as a by-product, new deviation estimates for the laws of two kinds of discrete stochastic approximation schemes. The first one refers to the law of an Euler like discretization scheme of a diffusion process at a fixed deterministic date and the second one concerns the law of a stochastic approximation algorithm at a given time-step. Our results notably improve and complete those obtained in [Frikha, Menozzi,2012]. The key point is to properly quantify the contribution of the diffusion term to the concentration regime. We also derive a general non-asymptotic deviation bound for the difference between a function of the trajectory of a continuous Euler scheme associated to a diffusion process and its mean. Finally, we obtain non-asymptotic bound for stochastic approximation with averaging of trajectories, in particular we prove that averaging a stochastic approximation algorithm with a slow decreasing step sequence gives rise to optimal concentration rate.

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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