A stochastic approximation algorithm with multiplicative step size adaptation
An algorithm of searching a zero of an unknown undimensional function is considered, measured at a point x with some error. The step sizes are random positive values and are calculated according to the rule: if two consecutive iterations are in same direction step is multiplied by u>1, otherwise, it is multiplied by 0 1, divergence. Due to the multiplicative rule of updating of the step, it is natural to expect that the sequence converges rapidly: like a geometric progression (if convergence takes place), but the limit value may not coincide with, but instead, approximates one of zeros of the function. By adjusting the parameters u and d, one can reach necessary precision of approximation; higher precision is obtained at the expense of lower convergence rate.