Line search filter inexact secant methods for nonlinear equality constrained optimization

We present inexact secant methods in association with line search filter technique for solving nonlinear equality constrained optimization. For large-scale applications, it is expensive to get an exact search direction, and hence we use an inexact method that finds an approximate solution satisfying some appropriate conditions. The global convergence of the proposed algorithm is established by using line search filter technique. The second-order correction step is used to overcome the Maratos effect, while the line search filter inexact secant methods have superlinear local convergence rate. Finally, the results of numerical experiments indicate that the proposed methods are efficient for the given test problems.