A derivative–free nonmonotone line search technique for unconstrained optimization
نویسندگان
چکیده
A tolerant derivative–free nonmonotone line search technique is proposed and analyzed. Several consecutive increases in the objective function and also non descent directions are admitted for unconstrained minimization. To exemplify the power of this new line search we describe a direct search algorithm in which the directions are chosen randomly. The convergence properties of this random method rely exclusively on the line search technique. We present numerical experiments, to illustrate the advantages of using a derivative-free nonmonotone globalization strategy, with approximated-gradient type methods and also with the inverse SR1 update that could produce non descent directions. In all cases we use a local variation finite differences approximation to the gradient.
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