An improved strongly sub-feasible SSLE method for optimization problems and numerical experiments

In this paper, the super-linearly and quadratically convergent strong sub-feasible method [J.L. Li, J.B. Jian, A superlinearly and quadratically convergent strongly subfeasible method for nonlinear inequality constrained optimization, OR Transactions, 7 (2) (2003) 21-34] for nonlinear inequality constrained optimization is improved, such that the iterative points can get into the feasible region after a finite number of iterations. As a result, a strict restricted condition can be overcome. Another two contributions of this paper are that a new bidirectional Armijo line search is presented and a lot of numerical comparison results are reported.