Greedy search method for separable nonlinear models using stage Aitken gradient descent and least squares algorithms

Aitken gradient descent (AGD) algorithm takes some advantages over the standard gradient descent (SGD) and Newton methods: (1) can achieve at least quadratic convergence in general; (2) does not require the Hessian matrix inversion; (3) has less computational efforts. When using the AGD method for a...

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Bibliographic Details
Published in:	IEEE transactions on automatic control Vol. 68; no. 8; pp. 1 - 8
Main Authors:	Chen, Jing, Mao, Yawen, Gan, Min, Wang, Dongqing, Zhu, Quanmin
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Aitken acceleration technique Algorithms Computational modeling Convergence convergence rate Cost function Eigenvalues and eigenfunctions Hessian matrices hierarchical identification algorithm Iterative algorithms Iterative methods Least squares Mathematical models Newton methods Parameter estimation Search methods separable nonlinear model
ISSN:	0018-9286, 1558-2523
Online Access:	Get full text
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