A Regularized Variable Projection Algorithm for Separable Nonlinear Least-Squares Problems

Separable nonlinear least-squares (SNLLS) problems arise frequently in many research fields, such as system identification and machine learning. The variable projection (VP) method is a very powerful tool for solving such problems. In this paper, we consider the regularization of ill-conditioned SNL...

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Bibliographic Details
Published in:	IEEE transactions on automatic control Vol. 64; no. 2; pp. 526 - 537
Main Authors:	Chen, Guang-Yong, Gan, Min, Chen, C. L. Philip, Li, Han-Xiong
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation algorithms Conditioning Data fitting Gallium nitride Iterative methods Jacobian matrices Least squares Linear programming Machine learning Machine learning algorithms Optimization Parameter estimation Regularization separable nonlinear least squares (SNLLS) Statistical models System identification variable projection (VP) weighted generalized cross validation (WGCV)
ISSN:	0018-9286, 1558-2523
Online Access:	Get full text
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