Trust Region SQP Filter Method for Equality Constrained Optimization
Wang Hua · Neimenggu Shi-da xuebao. Zhexue shehui kexue hanwen ban · 2008
The local convergence properties of the filter trust region algorithm was discussed.The filter approach can suffer from the so-called Maratos effect.Although the full Newton step may be a superlinear convergence step,it increases both the objective function and the constrained violation if the iteration point is arbitrarily close to a strict local solution of the NLP,and is therefore rejected by the algorithm.In this case the Maratos effect occurs and results in poor local convergence behavior.As a remedy,the infeasibility is improved in this paper.A second order correction is used if the full step is rejected.It is shown that this modification is indeed able to prevent the Maratos effect so that the algorithm can obtain the local superlinear convergence.