The MM algorithm is an iterative optimization method which exploits the convexity of a function in order to find its maxima or minima. The MM stands for “Majorize-Minimization” or “Minorize-Maximization”, depending on whether the desired optimization is a minimization or a maximization. Despite the name, MM itself is not an algorithm, but a description of how to construct an optimization algorithm.
The expectation–maximization algorithm can be treated as a special case of the MM algorithm.[1][2]
However, in the EM algorithm conditional expectations are usually involved, while in the MM algorithm convexity and inequalities are the main focus, and it is easier to understand and apply in most cases.[3]
^Lange, Kenneth. "The MM Algorithm" (PDF).
^Lange, Kenneth (2016). MM Optimization Algorithms. SIAM. doi:10.1137/1.9781611974409. ISBN 978-1-61197-439-3.
^Lange, K.; Zhou, H. (2022). "A legacy of EM algorithms". International Statistical Review. 90: S52–S66. doi:10.1111/insr.12526. PMC 10191373.
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