An efficient sequential quadratic programming algorithm for nonlinear programming

In this paper, a new feasible sequential quadratic programming (FSQP) algorithm is proposed to solve the nonlinear programming, where a feasible descent direction is obtained by solving only one QP subproblem. In order to avoid Maratos effect, a high-order revised direction is computed by solving a linear system with involving some “active” constraints. The theoretical analysis shows that global and superlinear convergence can be deduced.