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The delta-bar-delta algorithm is recognized as a learning rate adaptationtechnique that enhances the convergence speed of the training process inoptimization by dynamically scheduling the learning rate based on thedifference between the current and previous weight updates. While thisalgorithm has demonstrated strong competitiveness in full data optimizationwhen compared to other state-of-the-art algorithms like Adam and SGD, it mayencounter convergence issues in mini-batch optimization scenarios due to thepresence of noisy gradients.  In this study, we thoroughly investigate the convergence behavior of thedelta-bar-delta algorithm in real-world neural network optimization. To addressany potential convergence challenges, we propose a novel approach called RDBD(Regrettable Delta-Bar-Delta). Our approach allows for prompt correction ofbiased learning rate adjustments and ensures the convergence of theoptimization process. Furthermore, we demonstrate that RDBD can be seamlesslyintegrated with any optimization algorithm and significantly improve theconvergence speed.  By conducting extensive experiments and evaluations, we validate theeffectiveness and efficiency of our proposed RDBD approach. The resultsshowcase its capability to overcome convergence issues in mini-batchoptimization and its potential to enhance the convergence speed of variousoptimization algorithms. This research contributes to the advancement ofoptimization techniques in neural network training, providing practitionerswith a reliable automatic learning rate scheduler for achieving fasterconvergence and improved optimization outcomes.

An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent