A trust-region method by active-set strategy for general nonlinear optimization

The paper explores a trust-region active-set algorithm for general nonlinear optimization with nonlinear equality and inequality constraints. In this algorithm, an active-set strategy is used together with trust-region methods to compute the trial step. L 1 penalty functions are employed to obtain t...

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Bibliographic Details
Published inComputers & mathematics with applications (1987) Vol. 54; no. 2; pp. 229 - 241
Main Authors Ji, Ying, Zhang, Ke-Cun, Qu, Shao-Jian, Zhou, Ying
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.07.2007
Subjects
[formula omitted] penalty function
Active-set strategy
Algorithms
Convergence
General nonlinear optimization
Global convergence
Inequalities
Mathematical analysis
Mathematical models
Nonlinearity
Optimization
Strategy
Trust-region method
L 1 penalty function
Global convergence
General nonlinear optimization
Active-set strategy
Trust-region method
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Summary:The paper explores a trust-region active-set algorithm for general nonlinear optimization with nonlinear equality and inequality constraints. In this algorithm, an active-set strategy is used together with trust-region methods to compute the trial step. L 1 penalty functions are employed to obtain the global convergence. The global convergence of this algorithm is proved under standard conditions. The numerical tests show the efficiency of the proposed algorithm.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2007.02.003