Improved Feasible SQP Algorithm for Nonlinear Programs with Equality Constrained Sub-Problems

This paper proposed an improved feasible sequential quadratic programming (FSQP) method for nonlinear programs. As compared with the existing SQP methods which required solving the QP sub-problem with inequality constraints in single iteration, in order to obtain the feasible direction, the method of this paper is only necessary to solve an equality constrained quadratic programming sub-problems. Combined the generalized projection technique, a height-order correction direction is yielded by explicit formulas, which can avoids Maratos effect. Furthermore, under some mild assumptions, the algorithm is globally convergent and its rate of convergence is one-step superlinearly. Numerical results reported show that the algorithm in this paper is effective.