  MM (majorization--minimization) algorithms are an increasingly popular tool
for solving optimization problems in machine learning and statistical
estimation. This article introduces the MM algorithm framework in general and
via three popular example applications: Gaussian mixture regressions,
multinomial logistic regressions, and support vector machines. Specific
algorithms for the three examples are derived and numerical demonstrations are
presented. Theoretical and practical aspects of MM algorithm design are
discussed.
