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

Konstantinos Slavakis, Isao Yamada, Nobuhiko Ogura, Masahiro Yukawa

研究成果: Conference article査読

5 被引用数 (Scopus)

抄録

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 proposed method provides with a strongly convergent, asymptotically optimal point sequence as well as with a characterization of the limiting point. As a side effect, the method allows the asymptotic minimization over the nonempty intersection of a finite number of closed convex sets. Thus, new directions for set theoretic adaptive filtering algorithms are revealed whenever the estimandum (system to be identified) is known to satisfy a number of convex constraints. This leads to a unification of a wide range of set theoretic adaptive filtering schemes such as NLMS, Projected or Constrained NLMS, APA, Adaptive Parallel Subgradient Projection Algorithm, Adaptive Parallel Min-Max Projection Algorithm as well as their embedded constraint versions. Numerical results demonstrate the effectiveness of the proposed method to the problem of stereophonic acoustic echo cancellation.

本文言語English
ページ(範囲)960-964
ページ数5
ジャーナルConference Record - Asilomar Conference on Signals, Systems and Computers
1
出版ステータスPublished - 2004 12 1
外部発表はい
イベントConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
継続期間: 2004 11 72004 11 10

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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