A new self-adaptive trust region method for unconstrained optimization

Zhaocheng Cui; Boying Wu

doi:10.1177/1077546311408473

A new self-adaptive trust region method for unconstrained optimization

Zhaocheng Cui^*
, Boying Wu

^*Corresponding author for this work

School of Mathematics

Shandong Jiaotong University

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

In this paper, we propose a new self-adaptive trust region method for unconstrained optimization problems and develop some convergence properties. In our algorithm, we use the previous and current iterative information to define a suitable trust region radius at each iteration. The global and superlinear convergence properties of the algorithm are established under reasonable assumptions. Preliminary numerical results show that the new method is efficient and attractive for solving unconstrained optimization problems.

Original language	English
Pages (from-to)	1303-1309
Number of pages	7
Journal	JVC/Journal of Vibration and Control
Volume	18
Issue number	9
DOIs	https://doi.org/10.1177/1077546311408473
State	Published - Aug 2012

Keywords

Adaptive technique
global convergence
superlinear convergence
trust region method
unconstrained optimization

Access to Document

10.1177/1077546311408473

Cite this

@article{c672e1ca5b0f4d6890e113b345cfe285,

title = "A new self-adaptive trust region method for unconstrained optimization",

abstract = "In this paper, we propose a new self-adaptive trust region method for unconstrained optimization problems and develop some convergence properties. In our algorithm, we use the previous and current iterative information to define a suitable trust region radius at each iteration. The global and superlinear convergence properties of the algorithm are established under reasonable assumptions. Preliminary numerical results show that the new method is efficient and attractive for solving unconstrained optimization problems.",

keywords = "Adaptive technique, global convergence, superlinear convergence, trust region method, unconstrained optimization",

author = "Zhaocheng Cui and Boying Wu",

year = "2012",

month = aug,

doi = "10.1177/1077546311408473",

language = "英语",

volume = "18",

pages = "1303--1309",

journal = "JVC/Journal of Vibration and Control",

issn = "1077-5463",

publisher = "SAGE Publications Inc.",

number = "9",

}

TY - JOUR

T1 - A new self-adaptive trust region method for unconstrained optimization

AU - Cui, Zhaocheng

AU - Wu, Boying

PY - 2012/8

Y1 - 2012/8

N2 - In this paper, we propose a new self-adaptive trust region method for unconstrained optimization problems and develop some convergence properties. In our algorithm, we use the previous and current iterative information to define a suitable trust region radius at each iteration. The global and superlinear convergence properties of the algorithm are established under reasonable assumptions. Preliminary numerical results show that the new method is efficient and attractive for solving unconstrained optimization problems.

AB - In this paper, we propose a new self-adaptive trust region method for unconstrained optimization problems and develop some convergence properties. In our algorithm, we use the previous and current iterative information to define a suitable trust region radius at each iteration. The global and superlinear convergence properties of the algorithm are established under reasonable assumptions. Preliminary numerical results show that the new method is efficient and attractive for solving unconstrained optimization problems.

KW - Adaptive technique

KW - global convergence

KW - superlinear convergence

KW - trust region method

KW - unconstrained optimization

UR - https://www.scopus.com/pages/publications/84863841326

U2 - 10.1177/1077546311408473

DO - 10.1177/1077546311408473

M3 - 文章

AN - SCOPUS:84863841326

SN - 1077-5463

VL - 18

SP - 1303

EP - 1309

JO - JVC/Journal of Vibration and Control

JF - JVC/Journal of Vibration and Control

IS - 9

ER -

A new self-adaptive trust region method for unconstrained optimization

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this