The optimization algorithm and its hyperparameters can significantly affect the training speed and resulting model accuracy in machine learning applications. The wish list for an ideal optimizer includes fast and smooth convergence to low error, low computational demand, and general applicability. Our recently introduced continual resilient (CoRe) optimizer has shown superior performance compared to other state-of-the-art first-order gradient-based optimizers for training lifelong machine learning potentials. In this work we provide an extensive performance comparison of the CoRe optimizer and nine other optimization algorithms including the Adam optimizer and resilient backpropagation (RPROP) for diverse machine learning tasks. We analyze the influence of different hyperparameters and provide generally applicable values. The CoRe optimizer yields best or competitive performance in every investigated application, while only one hyperparameter needs to be changed depending on mini-batch or batch learning.

我們對CoRe優化器進行了廣泛的性能比較，並與其他九種優化算法進行了比較，包括Adam優化器和韌性反向傳播(RPROP)，結果顯示CoRe優化器在各種機器學習任務中表現最佳或競爭力非凡，而只需根據小批量或批量學習改變一個超參數。

CoRe优化器：机器学习的一体化解决方案