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
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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
Chun-ming Tang
中科院分区:
数学3区
文献类型:
--
作者:
Jin-bao Jian;Qing-juan Hu;Chun-ming Tang

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本文讨论了一类具有不等式约束的极大极小问题,并给出了该问题的另一种快速收敛方法。与以往的工作相比,本文提出的方法具有以下主要特点:首先,采用减少规模和计算量的主动集识别方法构造寻向子问题;其次,分别通过求解一类新的范数放松二次规划子问题和一组线性方程组计算主方向和高阶修正方向;第三,结合强子可行方向法和惩罚法,提出了一种新的直线搜索方法来确定步长。第四,在没有任何严格互补的温和假设下,可以得到全局收敛性和超线性收敛率。最后,给出了一些数值结果。
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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