An efficient variable projection formulation for separable nonlinear least squares problems
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Article number | 6553163 |
Pages (from-to) | 707-711 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 44 |
Issue number | 5 |
Online published | 3 Jul 2013 |
Publication status | Published - May 2014 |
Link(s)
Abstract
We consider in this paper a class of nonlinear least squares problems in which the model can be represented as a linear combination of nonlinear functions. The variable projection algorithm projects the linear parameters out of the problem, leaving the nonlinear least squares problems involving only the nonlinear parameters. To implement the variable projection algorithm more efficiently, we propose a new variable projection functional based on matrix decomposition. The advantage of the proposed formulation is that the size of the decomposed matrix may be much smaller than those of previous ones. The Levenberg-Marquardt algorithm using finite difference method is then applied to minimize the new criterion. Numerical results show that the proposed approach achieves significant reduction in computing time. © 2013 IEEE.
Research Area(s)
- Matrix decomposition, parameter estimation, separable nonlinear least squares problems, variable projection
Citation Format(s)
An efficient variable projection formulation for separable nonlinear least squares problems. / Gan, Min; Li, Han-Xiong.
In: IEEE Transactions on Cybernetics, Vol. 44, No. 5, 6553163, 05.2014, p. 707-711.
In: IEEE Transactions on Cybernetics, Vol. 44, No. 5, 6553163, 05.2014, p. 707-711.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review