  In this paper, we propose a new and efficient nonmonotone adaptive trust
region algorithm to solve unconstrained optimization problems. This algorithm
incorporates two novelties: it benefits from a radius dependent shrinkage
parameter for adjusting the trust region radius that avoids undesirable
directions and it exploits a new strategy to prevent sudden increments of
objective function values in nonmonotone trust region techniques. Global
convergence of this algorithm is investigated under some mild conditions.
Numerical experiments demonstrate the efficiency and robustness of the proposed
algorithm in solving a collection of unconstrained optimization problems from
the CUTEst package.
