A Penalty-Free Method with Trust Region for Nonlinear Semidefinite Programming
A Penalty-Free Method with Trust Region for Nonlinear Semidefinite Programming
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DOI：
10.1142/s0217595915400060
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
Zhongwen Chen;Shicai Miao
中科院分区：
文献类型：
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作者：
Zhongwen Chen;Shicai Miao
本文提出了一类新的无罚方法，它不使用任何罚函数或滤子来求解非线性半定规划（NSDP）。避免了惩罚参数的选择和滤波器集的存储。新方法采用信赖域框架计算试步。然后，根据一些可接受的标准，这取决于在非线性目标函数和约束不可行性的措施达到减少的试验步骤是接受或拒绝。在适当的假设下，我们证明了该算法是良好定义的，并且是全局收敛的。最后给出了初步的数值结果。
In this paper, we propose a class of new penalty-free method, which does not use any penalty function or a filter, to solve nonlinear semidefinite programming (NSDP). So the choice of the penalty parameter and the storage of filter set are avoided. The new method adopts trust region framework to compute a trial step. The trial step is then either accepted or rejected based on the some acceptable criteria which depends on reductions attained in the nonlinear objective function and in the measure of constraint infeasibility. Under the suitable assumptions, we prove that the algorithm is well defined and globally convergent. Finally, the preliminary numerical results are reported.