A perturbed successive quadratic programming algorithm and its convergence

In this paper, a successive quadratic programming algorithm for solving a nonlinear programming is introduced. Its specialty is that in each iteration of the algorithm, the constraints of the quadratic programming not only are the linear approximations of the original constraints, but also have a perturbation vector. Under certain conditions its locally R-linear rate of convergence is proved. In the final part of the paper, the conditions of its superlinear convergence is discussed. [In Chinese.]