full
search.png

Search

close.png

Cancel

arrow
Volume 21, Issue 2
A New Family of Trust Region Algorithms for Unconstrained Optimization

Yuhong Da & Dachuan Xu

J. Comp. Math., 21 (2003), pp. 221-228.

Published online: 2003-04

Preview Full PDF 2563 24736

Cited by

google scholar semantic scholar
Export citation
  • Abstract

Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.

  • Keywords

trust region method, global convergence, quasi-Newton method, unconstrained optimization, nonlinear programming.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JCM-21-221, author = {Yuhong Da and Dachuan Xu}, title = {A New Family of Trust Region Algorithms for Unconstrained Optimization}, journal = {Journal of Computational Mathematics}, year = {2003}, volume = {21}, number = {2}, pages = {221--228}, abstract = {

Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/10276.html} }
TY - JOUR T1 - A New Family of Trust Region Algorithms for Unconstrained Optimization AU - Yuhong Da & Dachuan Xu JO - Journal of Computational Mathematics VL - 2 SP - 221 EP - 228 PY - 2003 DA - 2003/04 SN - 21 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/10276.html KW - trust region method, global convergence, quasi-Newton method, unconstrained optimization, nonlinear programming. AB -

Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.

Yuhong Da and Dachuan Xu. (2003). A New Family of Trust Region Algorithms for Unconstrained Optimization. Journal of Computational Mathematics. 21 (2). 221-228. doi:
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard