不等式约束优化超线性收敛的信赖域-SQP算法

A Superlinearly Convergent Trust Region-SQP Algorithm for Inequality Constrained Optimization

本文讨论不等式约束优化问题,给出—个信赖域方法与SQP方法相结合的新算法.算法中的QP问题始终相容,并且QP问题产生的搜索方向始终为可行方向.采用高阶校正的方法来克服算法产生的Maratos效应现象.在适当的条件下,证明了算法的全局收敛性和超线性收敛性.数值结果表明算法是有效的.

In this paper, the inequality constrained problems are discussed, based on a combination technique of a trust region method and an SQP method, a new algorithm is proposed. In the algorithm the QP subproblems are always consistent and the direction of the QP subproblems is always a feasible direction.We use high order revised direction to avoid Maratos effect. Under some suitable conditions, the global and superlinear convergence can be induced. The numerical results show that the algorithm is effective.