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A Norm-relaxed SQP Algorithm with a System of Linear Equations for Constrained Minimax Problems
WANG Fusheng, GAO Juan, ZHAO Yuanlu, JIANG Hefeng
Acta Mathematicae Applicatae Sinica
2019, 42 (2):
242-253.
DOI: 10.12387/C2019020
Many problems of interest in real world applications can be modeled as a so-called minimax problem, it is of great value to study its theory and solving method. In this paper, a new SQP algorithm with a system of linear equations is proposed to solve minimax problems with equality and inequality constraints. First,based on the ε-active constraint set,a norm-relaxed quadratic programming subproblem and a system of linear equations are established to get the feasible direction of descent,which can overcome the Maratos effect, and greatly reduce the computational complexity of the algorithm; Second, a new curve search step-size strategy without the penalty factors or filters is introduced. The method not only avoids the choice of penalty factors, but also reduces the storage capacity of the computer. Finally, it is proved that,under appropriate assumptions, the new algorithm is globally convergent. Some preliminary numerical experiments are reported to show the effectiveness and competitiveness of the algorithm.
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