  Recently, Arjevani et al. [1] established a lower bound of iteration
complexity for the first-order optimization under an $L$-smooth condition and a
bounded noise variance assumption. However, a thorough review of existing
literature on Adam's convergence reveals a noticeable gap: none of them meet
the above lower bound. In this paper, we close the gap by deriving a new
convergence guarantee of Adam, with only an $L$-smooth condition and a bounded
noise variance assumption. Our results remain valid across a broad spectrum of
hyperparameters. Especially with properly chosen hyperparameters, we derive an
upper bound of the iteration complexity of Adam and show that it meets the
lower bound for first-order optimizers. To the best of our knowledge, this is
the first to establish such a tight upper bound for Adam's convergence. Our
proof utilizes novel techniques to handle the entanglement between momentum and
adaptive learning rate and to convert the first-order term in the Descent Lemma
to the gradient norm, which may be of independent interest.
