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Correction to: A sequential update algorithm for computing the stationary distribution vector in upper block-Hessenberg Markov chains

机译:校正至:一种顺序更新算法,用于计算上块-Hessenberg Markov链中的平稳分布矢量

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摘要

This paper proposes a new algorithm for computing the stationary distribution vector in continuous-time upper block-Hessenberg Markov chains. To this end, we consider the last-block-column-linearly-augmented (LBCL-augmented) truncation of the (infinitesimal) generator of the upper block-Hessenberg Markov chain. The LBCL-augmented truncation is a linearly augmented truncation such that the augmentation distribution has its probability mass only on the last block column. We first derive an upper bound for the total variation distance between the respective stationary distribution vectors of the original generator and its LBCL-augmented truncation. Based on the upper bound, we then establish a series of linear fractional programming (LFP) problems to obtain augmentation distribution vectors such that the bound converges to zero. Using the optimal solutions of the LFP problems, we construct a matrix-infinite-product (MIP) form of the original (i.e., not approximate) stationary distribution vector and develop a sequential update algorithm for computing the MIP form. Finally, we demonstrate the applicability of our algorithm to BMAP/M/ queues and M/M/s retrial queues.
机译:提出了一种计算连续时间上块Hessenberg Markov链中平稳分布矢量的新算法。为此,我们考虑了上块Hessenberg Markov链的(无穷小)生成器的最后一个块列线性增强(LBCL增强)截断。 LBCL增强的截断是线性增强的截断,因此,增强分布仅在最后一个块列上具有其概率质量。我们首先导出原始生成器及其LBCL增强的截断的各个静态分布矢量之间的总变化距离的上限。然后,基于上限,我们建立了一系列线性分数规划(LFP)问题,以获得增强分布矢量,以使边界收敛至零。使用LFP问题的最佳解决方案,我们构造了原始(即非近似)平稳分布矢量的矩阵无限积(MIP)形式,并开发了用于计算MIP形式的顺序更新算法。最后,我们演示了该算法对BMAP / M / s队列和M / M / s重试队列的适用性。

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