Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks
Gradient based optimization methods are nowadays the methods of choice for training deep neural networks (DNNs) in artificial intelligence (AI) systems. In practically relevant DNN training problems, one does usually not apply the standard gradient descent (GD) optimization method but instead one employs suitable sophisticated GD optimization methods, which incorporate adaptivity and/or acceleration techniques, such as the famous Adam optimizer. It is a key contribution of this work to provide a general unified convergence analysis for GD optimization methods in the training of DNNs with analytic activations such as the softplus and the popular Gaussian error linear unit (GeLU) activation...
| Authors | Shokhrukh Ibragimov, Arnulf Jentzen |
|---|---|
| Primary category | math.OC |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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