Newton-Type Algorithms with Nonmonotone Line Search for Large-Scale Unconstrained Optimization

In this paper we define globally convergent algorithms for the solution of large dimensional unconstrained minimization problems. The algorithms proposed employ a nonmonotone steplength selection rule along the search direction which is determined by means of a Truncated-Newton algorithm. Numerical results obtained for a set of test problems are reported