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A very simple unidimensional function with Lipschitz continuous gradient isconstructed such that the ADAM algorithm with constant stepsize, started fromthe origin, diverges when applied to minimize this function in the absence ofnoise on the gradient. Divergence occurs irrespective of the choice of themethod parameters.

Divergence of the ADAM algorithm with fixed-stepsize: a (very) simple example