  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, four 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.
