# An Introduction to MM Algorithms for Machine Learning and Statistical

arXiv: https://arxiv.org/abs/1611.03969 · HTML: https://mlsanity.com/paper/1611.03969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2016.

## Built on by (most cited first, 1 of 1)

- [An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling](https://mlsanity.com/paper/1707.03538/) (2017)

## Abstract (arXiv)

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.