A fast stochastic gradient algorithm: Maximal use of sparsification benefits under computational constraints

Masahiro Yukawa, Wolfgang Utschick

研究成果: Article査読

3 被引用数 (Scopus)

抄録

In this paper, we propose a novel stochastic gradient algorithm for efficient adaptive filtering. The basic idea is to sparsify the initial error vector and maximize the benefits from the sparsification under computational constraints. To this end, we formulate the task of algorithmdesign as a constrained optimization problem and derive its (non-trivial) closed-form solution. The computational constraints are formed by focusing on the fact that the energy of the sparsified error vector concentrates at the first few components. The numerical examples demonstrate that the proposed algorithm achieves the convergence as fast as the computationally expensive method based on the optimization without the computational constraints.

本文言語English
ページ(範囲)467-475
ページ数9
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E93-A
2
DOI
出版ステータスPublished - 2010 2月
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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