Local Convergence of SQP Methods for Mathematical Programs with Equilibrium Constraints
研究了序列二次规划方法求解带均衡约束数学规划问题的局部收敛性质，证明在强稳定点附近满足合理假设时方法超线性收敛，并举例说明部分假设难以放松。
Recently, nonlinear programming solvers have been used to solve a range of mathematical programs with equilibrium constraints (MPECs). In particular, sequential quadratic programming (SQP) methods have been very successful. This paper examines the local convergence properties of SQP methods applied to MPECs. SQP is shown to converge superlinearly under reasonable assumptions near a strongly stationary point. A number of examples are presented that show that some of the assumptions are difficult to relax.