A stochastic conjugate gradient method for the approximation of functions

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:236

Article

Jiang, Hong¹  Wilford, Paul¹ 

[1] Alcatel Lucent, Bell Labs, Murray Hill, NJ 07974 USA

关键词: Stochastic conjugate gradient;    Approximation of functions;    Convergence in probability;    Least squares solution;    Polynomial predistortion;    Power amplifier linearization;   

DOI  :  10.1016/j.cam.2011.12.012

来源: Elsevier

【 摘 要 】

A stochastic conjugate gradient method for the approximation of a function is proposed. The proposed method avoids computing and storing the covariance matrix in the normal equations for the least squares solution. In addition, the method performs the conjugate gradient steps by using an inner product that is based on stochastic sampling. Theoretical analysis shows that the method is convergent in probability. The method has applications in such fields as predistortion for the linearization of power amplifiers. (C) 2011 Elsevier B.V. All rights reserved.

【 授权许可】

Free