An ε-generalized gradient projection method for nonlinear minimax problems

In this paper, combining the techniques of ε -generalized gradient projection and Armjio’s line search, we present a new algorithm for the nonlinear minimax problems. At each iteration, the improved search direction is generated by an ε -generalized gradient projection explicit formula. Under some m...

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Bibliographic Details
Published inNonlinear dynamics Vol. 75; no. 4; pp. 693 - 700
Main Authors Ma, Guo-Dong, Jian, Jin-Bao
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.03.2014
Springer Nature B.V
Subjects
Algorithms
Automotive Engineering
Classical Mechanics
Control
Convergence
Dynamical Systems
Economic models
Engineering
Iterative methods
Mathematical models
Mechanical Engineering
Minimax technique
Nonlinear programming
Nonlinearity
Original Paper
Projection
Searching
Vibration
Nonlinear minimax problems
Global convergence
Strong convergence
generalized gradient projection method
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Summary:In this paper, combining the techniques of ε -generalized gradient projection and Armjio’s line search, we present a new algorithm for the nonlinear minimax problems. At each iteration, the improved search direction is generated by an ε -generalized gradient projection explicit formula. Under some mild assumptions, the algorithm possesses global and strong convergence. Finally, some preliminary numerical results show that the proposed algorithm performs efficiently.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-013-1095-1