PRIME: Phase Retrieval via Majorization-Minimization
PRIME: Phase Retrieval via Majorization-Minimization
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DOI：
10.1109/tsp.2016.2585084
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发表时间：
2015-11
影响因子：
5.4
通讯作者：
Tianyu Qiu;P. Babu;D. Palomar
中科院分区：
文献类型：
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作者：
Tianyu Qiu;P. Babu;D. Palomar
本文考虑了相位恢复问题，其中测量值仅由未知量的几个线性测量值的幅度组成，例如，时间序列的频谱分量。我们开发的优化最小化（MM）框架的基础上，具有上级性能的低复杂度算法。所提出的算法被称为PRIME：相位恢复vIa优化最小化技术。他们是首选现有的基准方法，因为在每次迭代一个简单的代理问题是解决了一个封闭形式的解决方案，单调减少原始的目标函数。总的来说，三个算法提出了使用不同的优化最小化技术。实验结果验证了我们的算法优于现有的方法在成功恢复和均方误差在各种设置。
This paper considers the phase retrieval problem in which measurements consist of only the magnitude of several linear measurements of the unknown, e.g., spectral components of a time sequence. We develop low-complexity algorithms with superior performance based on the majorization-minimization (MM) framework. The proposed algorithms are referred to as PRIME: Phase Retrieval vIa the Majorization-minimization techniquE. They are preferred to existing benchmark methods since at each iteration a simple surrogate problem is solved with a closed-form solution that monotonically decreases the original objective function. In total, three algorithms are proposed using different majorization-minimization techniques. Experimental results validate that our algorithms outperform existing methods in terms of successful recovery and mean-square error under various settings.