A feasible trust-region algorithm for inequality constrained optimization

The paper presents an algorithm for smooth nonlinearly inequality constrained optimization problems, in which a sequence of feasible iterates is generated by a trust-region sequential quadratic programming subproblem at each iteration. Because of retaining feasibility, the objective function can be used as a merit function and the subproblems are feasible. Under common assumptions, the algorithm is globally convergent. The numerical results show it is promising.