DocumentCode
2660228
Title
Notice of Retraction
SQP trust-region algorithm for finite minimax problems
Author
Fusheng Wang
Author_Institution
Dept. of Math., Taiyuan Normal Univ., Taiyuan, China
Volume
1
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Many real life problems can be stated as a minimax problem, such as economics, finance, management, engineering and other fields, which demonstrate the importance of having reliable methods to tackle minimax problems. In this paper, we develop a trust region algorithm for solving the problem of minimizing the maximum of a finite number of smooth functions, which does´t require the Hessian approximation matrix Bk keeping positive definite. Under usual conditions, the algorithm is of global convergence and locally superlinear convergence. Numerical results show that the algorithm is robust and efficient.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Many real life problems can be stated as a minimax problem, such as economics, finance, management, engineering and other fields, which demonstrate the importance of having reliable methods to tackle minimax problems. In this paper, we develop a trust region algorithm for solving the problem of minimizing the maximum of a finite number of smooth functions, which does´t require the Hessian approximation matrix Bk keeping positive definite. Under usual conditions, the algorithm is of global convergence and locally superlinear convergence. Numerical results show that the algorithm is robust and efficient.
Keywords
Hessian matrices; approximation theory; convergence of numerical methods; minimax techniques; quadratic programming; Hessian approximation matrix; SQP trust region algorithm; finite minimax problems; locally superlinear convergence; sequential quadratic programming; smooth functions finite number; Convergence; Curve fitting; Design engineering; Finance; Minimax techniques; Nonlinear equations; Optimization methods; Reliability engineering; Robustness; Search methods; Algorithms; Convergence analysis; Minimax problems; Nonsmooth optimization; Sequential quadratic programming;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485989
Filename
5485989
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