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Fast solvers for queueing systems with negative customers

机译:用于带负客户的排队系统的快速求解器

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In this paper, we are interested in solving queueing systems having Poisson batch arrivals, exponential servers and negative customers. Preconditioned Conjugate Gradient (PCG) method is applied to solving the steady-state probability distribution of the queueing system. Preconditioners are constructed by exploiting near-Toeplitz structure of the generator matrix and the Gohberg-Semumcul formula. We proved that the preconditioned system has singular values clustered around one. Therefore Conjugate Gradient (CG) methods when applied to solving the preconditioned system, we expect fast convergence rate. Numerical examples are given to demonstrate our claim.
机译:在本文中,我们有兴趣求解有泊松批评人数,指数服务器和负客户的排队系统。预处理共轭梯度(PCG)方法应用于解决排队系统的稳态概率分布。通过利用发电机矩阵和Gohberg-Semumcul公式的近趾结构来构建预处理器。我们证明预处理系统有一个奇异值聚集在一起。因此,缀合物梯度(CG)方法应用于解决预处理系统时,我们预计会收敛速度快。给出了数值例子来证明我们的索赔。

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