Adaptive projected subgradient method and set theoretic adaptive filtering with multiple convex constraints

This paper presents an algorithmic solution, the adaptive projected subgradient method, to the problem of asymptotically minimizing a certain sequence of nonnegative continuous convex functions over the fixed point set of strongly attracting nonexpansive mappings in a real Hilbert space. The propose...

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Bibliographic Details
Published in:	2004 38th Asilomar Conference on Signals, Systems and Computers Vol. 1; pp. 960 - 964 Vol.1
Main Authors:	Slavakis, K., Yamada, I., Ogura, N., Yukawa, M.
Format:	Conference Proceeding
Language:	English
Published:	Piscataway NJ IEEE 2004
Subjects:	Adaptive filters Applied sciences Constraint theory Convergence Detection, estimation, filtering, equalization, prediction Echo cancellers Exact sciences and technology Filtering algorithms Hilbert space Information, signal and communications theory Miscellaneous Projection algorithms Resonance light scattering Signal and communications theory Signal processing Signal processing algorithms Signal, noise Telecommunications and information theory Working environment noise Performance evaluation Parallel algorithm Convex set Adaptive algorithm Maximin problem Stereophony Algorithmics Adaptive filtering Continuous function Mapping Acoustic signal processing Adaptive method Minimax method Unification Numerical simulation Set theory Hilbert space Convex function Asymptotic approximation Least mean squares methods Fixed point
ISBN:	0780386221, 9780780386228
Online Access:	Get full text
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