Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

Ying Sun, Prabhu Babu, Daniel P. Palomar

Research output: Contribution to journalArticlepeer-review

952 Scopus citations


This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also covered. To bridge the gap between theory and practice, upperbounds for a large number of basic functions, derived based on the Taylor expansion, convexity, and special inequalities, are provided as ingredients for constructing surrogate functions. With the pre-requisites established, the way of applying MM to solving specific problems is elaborated by a wide range of applications in signal processing, communications, and machine learning.

Original languageEnglish (US)
Article number7547360
Pages (from-to)794-816
Number of pages23
JournalIEEE Transactions on Signal Processing
Issue number3
StatePublished - Feb 1 2017

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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