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Convergence analysis of a proximal Gauss-Newton method


  • Saverio Salzo


  • Silvia Villa



An extension of the Gauss-Newton algorithm is proposed to find local minimizers of penalized nonlinear least squares problems, under generalized Lipschitz assumptions. Convergence results of local type are obtained, as well as an estimate of the radius of the convergence ball. Some applications for solving constrained nonlinear equations are discussed and the numerical performance of the method is assessed on some significant test problems. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Saverio Salzo & Silvia Villa, 2012. "Convergence analysis of a proximal Gauss-Newton method," Computational Optimization and Applications, Springer, vol. 53(2), pages 557-589, October.
  • Handle: RePEc:spr:coopap:v:53:y:2012:i:2:p:557-589 DOI: 10.1007/s10589-012-9476-9

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    References listed on IDEAS

    1. Chao Zhang & Xiaojun Chen & Naihua Xiu, 2009. "Global error bounds for the extended vertical LCP," Computational Optimization and Applications, Springer, vol. 42(3), pages 335-352, April.
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