A simple feasible SQP algorithm for inequality constrained optimization

In this paper, an SQP method generating feasible iterates is presented to solve the nonlinear inequality constrained optimization problems. A new modified method which ensures the global and superlinear convergence is proposed by solving systems of linear equation, instead of QP subproblems and linear squares problems. Here the search direction is a suitable combination of a descent direction, a feasible direction and a second-order revised direction. The theoretical analysis shows that global and superlinear convergence can be induced under some suitable conditions.