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

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 result...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 65; no. 3; pp. 794 - 816
Main Authors:	Ying Sun, Babu, Prabhu, Palomar, Daniel P.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Basic converters Convergence Convexity Estimation Linear programming Machine learning Majorization-minimization Minimization non-convex optimization Optimization Signal processing Signal processing algorithms surrogate function Taylor series Upper bounds upperbounds
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!