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Poster
Adam Can Converge Without Any Modification On Update Rules
Yushun Zhang · Congliang Chen · Naichen Shi · Ruoyu Sun · Zhi-Quan Luo

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #825
in Poster Session 6 »
Ever since \citet{reddi2019convergence} pointed out the divergence issue of Adam, many new variants have been designed to obtain convergence. However, vanilla Adam remains exceptionally popular and it works well in practice. Why is there a gap between theory and practice? We point out there is a mismatch between the settings of theory and practice: \citet{reddi2019convergence} pick the problem after picking the hyperparameters of Adam, i.e., $(\beta_1,\beta_2)$; while practical applications often fix the problem first and then tune $(\beta_1,\beta_2)$. Due to this observation, we conjecture that the empirical convergence can be theoretically justified, only if we change the order of picking the problem and hyperparameter. In this work, we confirm this conjecture. We prove that, when the 2nd-order momentum parameter $\beta_2$ is large and 1st-order momentum parameter $\beta_1 < \sqrt{\beta_2}<1$, Adam converges to the neighborhood of critical points. The size of the neighborhood is propositional to the variance of stochastic gradients. Under an extra condition (strong growth condition), Adam converges to critical points. It is worth mentioning that our results cover a wide range of hyperparameters: as $\beta_2$ increases, our convergence result can cover any $\beta_1 \in [0,1)$ including $\beta_1=0.9$, which is the default setting in deep learning libraries. To our knowledge, this is the first result showing that Adam can converge {\it without any modification} on its update rules. Further, our analysis does not require assumptions of bounded gradients or bounded 2nd-order momentum. When $\beta_2$ is small, we further point out a large region of $(\beta_1,\beta_2)$ combinations where Adam can diverge to infinity. Our divergence result considers the same setting (fixing the optimization problem ahead) as our convergence result, indicating that there is a phase transition from divergence to convergence when increasing $\beta_2$. These positive and negative results provide suggestions on how to tune Adam hyperparameters: for instance, when Adam does not work well, we suggest tuning up $\beta_2$ and trying $\beta_1< \sqrt{\beta_2}$.
Keywords: Deep Learning · Optimization · adam
[ Paper]  [ Slides]  [ Poster]  [ OpenReview] 

Author Information

Yushun Zhang (The Chinese University of Hong Kong, Shenzhen)

I am a Ph.D. student under the supervision of Prof. Tom Zhi-Quan Luo and Prof. Tong Zhang, I am interested in understanding deep learning.

Congliang Chen (The Chinese University of Hong Kong(Shenzhen))
Naichen Shi (University of Michigan)
Ruoyu Sun (Chinese University of Hong Kong (Shenzhen))
Zhi-Quan Luo (University of Minnesota, Twin Cites)

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