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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on signal processing Ročník 65; číslo 3; s. 794 - 816
Hlavní autori:	Ying Sun, Babu, Prabhu, Palomar, Daniel P.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!