论文标题
传递算法的近似消息的通用性
Universality of Approximate Message Passing Algorithms
论文作者
论文摘要
我们考虑了一类广泛的近似消息传递（AMP）算法，该算法以$ n \ times n $随机对称矩阵$ a $ a $定义为Lipschitzian功能迭代。我们在$ n $ limit中为此放大器建立了噪声的普遍性，并在许多AMP中验证了这种行为，这些AMP通常适用于压缩感应，统计推断和旋转眼镜的优化。
We consider a broad class of Approximate Message Passing (AMP) algorithms defined as a Lipschitzian functional iteration in terms of an $n\times n$ random symmetric matrix $A$. We establish universality in noise for this AMP in the $n$-limit and validate this behavior in a number of AMPs popularly adapted in compressed sensing, statistical inferences, and optimizations in spin glasses.
