This paper deals with gradient methods for minimizing n-dimensional strictly convex quadratic functions. Two new adaptive stepsize selection rules are presented and some key properties are proved. Practical insights on the effectiveness of the proposed techniques are given by a numerical comparison with the Barzilai-Borwein (BB) method, the cyclic/adaptive BB methods and two recent monotone gradient methods.

In recent years several proposals for the step-size selection have largely improved the gradient methods, in the case of both constrained and unconstrained nonlinear optimization. We introduce a new step-size rule with some crucial properties. We design step-size selection strategies where the new rule and a standard Barzilai-Borwein (BB) rule can be adaptively alternated to get meaningful convergence rate improvements in comparison with other BB-like gradient schemes.