Approximate Gauss–Newton methods for solving underdetermined nonlinear least squares problems

  • Ji Feng Bao
  • , Chong Li
  • , Wei Ping Shen
  • , Jen Chih Yao
  • , Sy Ming Guu*
  • *Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

29 Scopus citations

Abstract

We propose several approximate Gauss–Newton methods, i.e., the truncated, perturbed, and truncated-perturbed GN methods, for solving underdetermined nonlinear least squares problems. Under the assumption that the Fréchet derivatives are Lipschitz continuous and of full row rank, Kantorovich-type convergence criteria of the truncated GN method are established and local convergence theorems are presented with the radii of convergence balls obtained. As consequences of the convergence results for the truncated GN method, convergence theorems of the perturbed and truncated-perturbed GN methods are also presented. Finally, numerical experiments are presented where the comparisons with the standard inexact Gauss–Newton method and the inexact trust-region method for bound-constrained least squares problems [23] are made.

Original languageEnglish
Pages (from-to)92-110
Number of pages19
JournalApplied Numerical Mathematics
Volume111
DOIs
StatePublished - 01 01 2017

Bibliographical note

Publisher Copyright:
© 2016 IMACS

Keywords

  • Approximate Gauss–Newton methods
  • Lipschitz condition
  • Nonlinear least squares problems

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