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Randomized Kaczmarz algorithms: Exact MSE analysis and optimal sampling probabilities

机译:随机Kaczmarz算法:精确的MSE分析和最佳采样概率

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The Kaczmarz method, or the algebraic reconstruction technique (ART), is a popular method for solving large-scale overdetermined systems of equations. Recently, Strohmer et al. proposed the randomized Kaczmarz algorithm, an improvement that guarantees exponential convergence to the solution. This has spurred much interest in the algorithm and its extensions. We provide in this paper an exact formula for the mean squared error (MSE) in the value reconstructed by the algorithm. We also compute the exponential decay rate of the MSE, which we call the "annealed" error exponent. We show that the typical performance of the algorithm is far better than the average performance. We define the "quenched" error exponent to characterize the typical performance. This is far harder to compute than the annealed error exponent, but we provide an approximation that matches empirical results. We also explore optimizing the algorithm's row-selection probabilities to speed up the algorithm's convergence.
机译:Kaczmarz方法或代数重建技术(ART)是解决大型超定方程组的一种流行方法。最近,Strohmer等人。提出了随机的Kaczmarz算法,该改进可确保对解决方案进行指数收敛。这引起了人们对该算法及其扩展的极大兴趣。我们在本文中提供了算法重构值中均方误差(MSE)的精确公式。我们还计算了MSE的指数衰减率,我们称其为“退火”误差指数。我们证明了该算法的典型性能远胜于平均性能。我们定义“淬火”误差指数以表征典型性能。这比退火误差指数要难得多,但是我们提供了与经验结果相匹配的近似值。我们还将探索优化算法的行选择概率,以加快算法的收敛速度。

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