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

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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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  -