A Stochastic Conjugate Gradient Method for Approximation of Functions
Numerical Analysis
2013-02-11 v1
Abstract
A stochastic conjugate gradient method for 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 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.
Cite
@article{arxiv.1302.1945,
title = {A Stochastic Conjugate Gradient Method for Approximation of Functions},
author = {Hong Jiang and Paul Wilford},
journal= {arXiv preprint arXiv:1302.1945},
year = {2013}
}
Comments
21 pages, 5 figures