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Regularization methods for uniformly rank-deficient nonlinear least-squares problems
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
2005 (English)In: Journal of Optimization Theory and Applications, ISSN 0022-3239, Vol. 127, no 1, 1-26 p.Article in journal (Refereed) Published
Abstract [en]

In solving the nonlinear least-squares problem of minimizing ||f(x)||22, difficulties arise with standard approaches, such as the Levenberg-Marquardt approach, when the Jacobian of f is rank-deficient or very ill-conditioned at the solution. To handle this difficulty, we study a special class of least-squares problems that are uniformly rank-deficient, i.e., the Jacobian of f has the same deficient rank in the neighborhood of a solution. For such problems, the solution is not locally unique. We present two solution tecniques: (i) finding a minimum-norm solution to the basic problem, (ii) using a Tikhonov regularization. Optimality conditions and algorithms are given for both of these strategies. Asymptotical convergence properties of the algorithms are derived and confirmed by numerical experiments. Extensions of the presented ideas make it possible to solve more general nonlinear least-squares problems in which the Jacobian of f at the solution is rank-deficient or ill-conditioned.

Place, publisher, year, edition, pages
2005. Vol. 127, no 1, 1-26 p.
Keyword [en]
Nonlinear least squares - Gauss-Newton Method - rank-deficient matrices - minimum norm problems - truncation problems - stabilization methods - ill-posed problems - Tikhonov regularization
National Category
Mathematics
Identifiers
URN: urn:nbn:se:miun:diva-3967DOI: 10.1007/s10957-005-6389-0ISI: 000232059800001Local ID: 4382OAI: oai:DiVA.org:miun-3967DiVA: diva2:28999
Available from: 2008-09-30 Created: 2008-09-30Bibliographically approved

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Gulliksson, Mårten
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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
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  • asciidoc
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