Abstract Adaptive moment estimation algorithms with momentum and adaptive step techniques are widely applied in deep learning. However, these algorithms cannot achieve the optimal performance in both theory and experiment. To solve the problem, an AdaBelief based heavy-ball momentum method, AdaBHB, is proposed. The AdaBelief technique of adjusting step size flexibly is introduced to improve the algorithm performance in experiments. The heavy ball momentum method with step size adjusted by exponential moving average strategy is employed to accelerate convergence. According to the convergence analysis techniques of AdaBelief and Heavy-ball momentum methods, time-varying step size and momentum coefficient are selected skillfully and the momentum term and adaptive matrix are added. It is proved that AdaBHB gains the optimal individual convergence rate for non-smooth general convex optimization problems. Finally, the correctness of the theoretical analysis of the proposed algorithm is verified by experiments on convex optimization problems and deep neural networks, and AdaBHB is validated to obtain the optimal convergence in theory with performance improved.
Fund:Supported by National Natural Science Foundation of China(No.62076252)
Corresponding Authors:
TAO Qing, Ph.D., professor. His research interests include pa-ttern recognition, machine learning and applied mathematics.
About author:: ZHANG Zedong, master student. His research interests include pattern recognition and machine learning;LONG Sheng, master student. His research interests include pattern recognition and machine learning;BAO Lei, Ph.D., lecturer. Her research interests include pattern recognition and computer vision.
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2022, Vol. 35 
