Adaptive step size random search
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TL;DR
A practical adaptive step size random search algorithm is proposed, and experimental experience shows the superiority of random search over other methods for sufficiently high dimension.
Abstract
Absiraci-Fixed step size random search for minimization of functions of several parameters is described and compared with the k e d step size gradient method for a particular surface. A theoretical technique, using the optimum step size at each step, is analyzed. A practical adaptive step size random search algorithm is then pro-posed, and experimental experience is reported that shows the superiority of random search over other methods for sufllciently high dimension. T