Secant algorithms with nonmonotone trust region that employs fletcher penalty function for constrained optimization

In this paper, we present a family of secant algorithms in association with nonmonotone trust region strategy for nonlinear equality constrained optimization problems. The proposed algorithms are globally convergent while keeping the local superlinear rate by introducing Fletcher's penalty function as merit function.