Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)

A Larger Step-Size Line Search Self-adaptive Trust Region Method and Its Convergence

Authors
Lin Feng
Corresponding Author
Lin Feng
Available Online December 2016.
DOI
10.2991/msota-16.2016.71How to use a DOI?
Keywords
unconstrained optimization; trust region method; self-adaptive; larger step-size line search; convergence
Abstract

In this paper, we give a trust region method for unconstrained optimization, in which its radius is automatically updated by the current information. And when the trial step is rejected, we apply a larger step-size line search to the method. The primary aim is to update the radius depending on the problem itself at each iteration. The next aim is to avoid resolving the subproblem, which decreases computation load. The global convergence as well as the local convergence of the method is analyzed under certain conditions.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-284-8
ISSN
2352-538X
DOI
10.2991/msota-16.2016.71How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Lin Feng
PY  - 2016/12
DA  - 2016/12
TI  - A Larger Step-Size Line Search Self-adaptive Trust Region Method and Its Convergence
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 330
EP  - 334
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.71
DO  - 10.2991/msota-16.2016.71
ID  - Feng2016/12
ER  -