Superlinearly Convergent Norm-Relaxed SQP Method Based on Active Set Identification and New Line Search for Constrained Minimax Problems
Superlinearly Convergent Norm-Relaxed SQP Method Based on Active Set Identification and New Line Search for Constrained Minimax Problems
复制标题
基于活动集辨识和新线搜索的约束极小极大问题的超线性收敛范数松弛SQP方法
DOI:
10.1007/s10957-013-0503-5
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发表时间:
2013-12
影响因子:
1.9
通讯作者:
Chun-ming Tang
中科院分区:
文献类型:
--
作者:
Jin-bao Jian;Qing-juan Hu;Chun-ming Tang
In this paper, the minimax problems with inequality constraints are discussed, and an alternative fast convergent method for the discussed problems is proposed. Compared with the previous work, the proposed method has the following main characteristics. First, the active set identification which can reduce the scale and the computational cost is adopted to construct the direction finding subproblems. Second, the master direction and high-order correction direction are computed by solving a new type of norm-relaxed quadratic programming subproblem and a system of linear equations, respectively. Third, the step size is yielded by a new line search which combines the method of strongly sub-feasible direction with the penalty method. Fourth, under mild assumptions without any strict complementarity, both the global convergence and rate of superlinear convergence can be obtained. Finally, some numerical results are reported.
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