A stochastic conjugate gradient method for the approximation of functions
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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.
论文关键词:Stochastic conjugate gradient,Approximation of functions,Convergence in probability,Least squares solution,Polynomial predistortion,Power amplifier linearization
论文评审过程:Received 2 April 2010, Revised 7 September 2011, Available online 29 December 2011.
论文官网地址:https://doi.org/10.1016/j.cam.2011.12.012