An Adaptive Nonmonotone Trust Region Method Based on a Structured Quasi Newton Equation for the Nonlinear Least Squares Problem
Keywords:
Trust region, Least Squares Problem, Structured Secant ApproximationAbstract
In this work an iterative method to solve the nonlinear least squares problem is presented. The algorithm combines a secant method with a strategy of nonmonotone trust region. In order to dene the quadratic model, the Hessian matrix is chosen using a secant approach that takes advantage of the structure of the problem, and the radius of the trust region is updated following an adaptive technique. Moreover, convergence properties of this algorithm are proved. The numerical experimentation, in which several ways of choosing the Hessian matrix are compared, shows the effiency and robustness of the method.
Downloads
Published
Issue
Section
License
Copyright (c) 2017 Graciela Croceri, Gonzalo Pizarro, Graciela Sottosanto

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Those authors who have publications with this journal, agree with the following terms:
a. Authors will retain its copyright and will ensure the rights of first publication of its work to the journal, which will be at the same time subject to the Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) allowing third parties to share the work as long as the author and the first publication on this journal is indicated.
b. Authors may elect other non-exclusive license agreements of the distribution of the published work (for example: locate it on an institutional telematics file or publish it on an monographic volume) as long as the first publication on this journal is indicated,
c. Authors are allowed and suggested to disseminate its work through the internet (for example: in institutional telematics files or in their website) before and during the submission process, which could produce interesting exchanges and increase the references of the published work. (see The effect of open Access)















