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Generating functional analysis of iterative algorithms for compressed sensing

机译:生成压缩感知迭代算法的功能分析

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摘要

It has been shown that approximate message passing algorithm is effective in reconstruction problems for compressed sensing. To evaluate dynamics of such an algorithm, the state evolution (SE) has been proposed. If an algorithm can cancel the correlation between the present messages and their past values, SE can accurately tract its dynamics via a simple one-dimensional map. In this paper, we focus on dynamics of algorithms which cannot cancel the correlation and evaluate it by the generating functional analysis (GFA), which allows us to study the dynamics by an exact way in the large system limit.
机译:已经表明,近似消息传递算法在压缩感测的重构问题中是有效的。为了评估这种算法的动态性,提出了状态演化(SE)。如果算法可以取消当前消息与其过去值之间的相关性,则SE可以通过简单的一维映射来准确地把握其动态。在本文中,我们专注于无法取消相关性的算法动力学,并通过生成函数分析(GFA)对其进行评估,这使我们能够在较大的系统范围内以精确的方式研究动力学。

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