MULTIPLE USE OF BACKTRACKING LINE SEARCH IN UNCONSTRAINED OPTIMIZATION

The gradient method is a very efficient iterative technique for solving unconstrained optimization problems. Motivated by recent modifications of some variants the SM method, this study proposed two methods that are globally convergent as well computationally efficient. Each under influence backtracking line search. Results obtained from numerical implementation these and performance profiling show competitive with well-known traditional methods.