  A very simple unidimensional function with Lipschitz continuous gradient is
constructed such that the ADAM algorithm with constant stepsize, started from
the origin, diverges when applied to minimize this function in the absence of
noise on the gradient. Divergence occurs irrespective of the choice of the
method parameters.
