On the superlinear local convergence of a penalty-free method for nonlinear semidefinite programming

On the superlinear local convergence of a penalty-free method for nonlinear semidefinite programming
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非线性半定规划无罚分法的超线性局部收敛

DOI:
10.1016/j.cam.2016.05.007
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发表时间:
2016-12
影响因子:
2.4
通讯作者:
Zhongwen Chen
Zhongwen Chen
中科院分区:
数学2区
文献类型:
--
作者:
Qi Zhao;Zhongwen Chen

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本文提出了一种求解非线性半定规划问题的序列半定规划算法,该算法不使用罚函数和滤子。该方法受经典SQP方法的启发,在每次迭代中通过一个二次半定规划子问题计算一个试步。确定试验步长,使得目标函数的值或约束违反的度量充分减小。为了保证全局收敛性,每次迭代中约束违反的度量要求不超过一个递减的限制。在较弱的假设下证明了算法的全局收敛性。我们还分析了所提出的方法的本地行为,同时使用二阶校正策略,以避免Maratos效应。证明了在严格互补和强二阶充分条件下,该算法的局部收敛速度是超线性的。最后,给出了控制器设计问题的非线性半定规划公式的数值结果,其数据包含在C O M P l e i B中。
This paper is concerned with a sequentially semidefinite programming (SSDP) algorithm for solving nonlinear semidefinite programming problems (NLSDP), which does not use a penalty function or a filter. This method, inspired by the classic SQP method, calculates a trial step by a quadratic semidefinite programming subproblem at each iteration. The trial step is determined such that either the value of the objective function or the measure of constraint violation is sufficiently reduced. In order to guarantee global convergence, the measure of constraint violation in each iteration is required not to exceed a progressively decreasing limit. We prove the global convergence properties of the algorithm under mild assumptions. We also analyze the local behaviour of the proposed method while using a second order correction strategy to avoid Maratos effect. We prove that, under the strict complementarity and the strong second order sufficient conditions with the sigma term, the rate of local convergence is superlinear. Finally, some numerical results with nonlinear semidefinite programming formulation of control design problem with the data contained in C O M P l e i b are given.
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