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Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 038341 (2007) Cite this article

Abstract

We propose a robust adaptive algorithm for generalized eigendecomposition problems that arise in modern signal processing applications. To that extent, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Starting from the proposed cost function and making use of an approximation of the Hessian matrix, a robust modified Newton algorithm is derived. A rigorous analysis of its convergence properties is presented by using stochastic approximation theory. We also apply this theory to solve the signal reception problem of multicarrier DS-CDMA to illustrate its practical application. The simulation results show that the proposed algorithm has fast convergence and excellent tracking capability, which are important in a practical time-varying communication environment.

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Authors and Affiliations

  1. Laboratory of Network Communication System and Control, Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230027, China

    Jian Yang, Feng Yang, Hong-Sheng Xi, Wei Guo & Yanmin Sheng

Authors
  1. Jian Yang
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  2. Feng Yang
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  3. Hong-Sheng Xi
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  4. Wei Guo
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  5. Yanmin Sheng
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Correspondence to Jian Yang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Yang, J., Yang, F., Xi, HS. et al. Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application. EURASIP J. Adv. Signal Process. 2007, 038341 (2007). https://doi.org/10.1155/2007/38341

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  • DOI: https://doi.org/10.1155/2007/38341

Keywords

  • Nonlinear Optimization
  • Hessian Matrix
  • Stochastic Approximation
  • Adaptive Modify
  • Nonlinear Optimization Problem