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CONVERGENCE ANALYSIS ON A FAST ITERATIVE PHASE RETRIEVAL ALGORITHM WITHOUT INDEPENDENCE ASSUMPTION

机译:没有独立假设的快速迭代相位检索算法的收敛性分析

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Phase retrieval has been an attractive problem, and many algorithms have been proposed. Randomized Kaczmarz method is a fast iterative method with good performance in both convergence rate and computational cost with theoretical analysis. However, they all assume that the iteratively updated variables and the original data are independent, which is not true in reality. In this paper, we study for the first time the convergence analysis of this method in the real case, where only a finite number of measurements are available. Specifically, we theoretically prove the linear rate of convergence for the phase retrieval via randomized Kaczmarz algorithm without independence assumption.
机译:阶段检索是一个有吸引力的问题,并且已经提出了许多算法。随机KACZMARZ方法是一种快速迭代方法,具有良好的良好性能,既具有理论分析的收敛速度和计算成本。但是,它们都假设迭代更新的变量和原始数据是独立的,这在现实中不是真的。在本文中,我们首次研究了在实际情况下对该方法的收敛分析,其中仅提供有限数量的测量。具体而言,我们理论上证明了通过随机kaczmarz算法的相位检索的阶段检索的线性速率,而没有独立的假设。

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