I read that gradient descent converge always to a local minimum while other methods as Newton's method this is not guaranteed (if the Hessian is not definite positive); but if the start point in GD is unfortunately a local maximum (and then the derivative is zero), how we can say that it converge to a (local/global) minimum? does not it stall?