For approximate inference in the generalized quadratic equations model, many state-of-the-art algorithms lack any prior knowledge of the target signal structure, exhibits slow convergence, and can not handle any analytic prior knowledge of the target signal structure. So, this paper proposes a new algorithm, Quadratic Message passing (QMP). QMP has a complexity as low as $O(N^{3})$. The SE derived for QMP can capture precisely the per-iteration behavior of the simulated algorithm. Simulation results confirm QMP outperforms many state-of-the-art algorithms.

翻译：针对广义二次方程模型中的近似推理，许多现有算法缺乏对目标信号结构的先验知识，存在收敛速度慢、无法处理任何目标信号结构的解析先验信息等问题。为此，本文提出一种新算法——二次消息传递（QMP）。QMP的计算复杂度低至$O(N^{3})$。基于QMP推导出的状态演化（SE）可精确捕捉仿真算法每轮迭代的行为特征。仿真结果证实，QMP的性能优于众多现有算法。