Local Convergence of Trust Region Filter SQP Method
Shaoyi Zhang · Science Technology and Engineering · 2008
The local convergence properties of the filter trust region algorithm are discussed.The filter approach can suffer from the so-called Maratos effect.The Maratos effect occurs if,arbitrarily close to a strict local solution of the NLP,a full Newton step increases both the objective function and the constraint violation,and is therefore rejected by the filter,even though it could be a very good step toward the solution.This can result in poor local convergence behavior.As a remedy,if the full Newton step is rejected,by means of a seconde order correction which aims to further reduce infeasibility.This modification is indeed able to prevent the Maratos effect.