JIMO
New adaptive stepsize selections in gradient methods
Giacomo Frassoldati Luca Zanni Gaetano Zanghirati
This paper deals with gradient methods for minimizing $n$-dimen-sional 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.
keywords: gradient methods Unconstrained optimization strictly convex quadratics adaptive stepsize selections.

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