AbstractThe paper deals with a class of random‐search‐algorithms representing a generalization of the deterministic gradient algorithm in such a way that the gradient direction is replaced by the direction of a random vector uniformly distributed on the unit hypersphere. It is shown that under weak assumptions on the objective function the linear convergence rate of the gradient method can be transferred to these stochastic algorit