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arXiv · 2312.13677

Parallel Trust-Region Approaches in Neural Network Training: Beyond Traditional Methods

Ken Trotti·Samuel A. Cruz Alegría·Alena Kopaničáková·Rolf Krause
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Abstract

We propose to train neural networks (NNs) using a novel variant of the ``Additively Preconditioned Trust-region Strategy'' (APTS). The proposed method is based on a parallelizable additive domain decomposition approach applied to the neural network's parameters. Built upon the TR framework, the APTS method ensures global convergence towards a minimizer. Moreover, it eliminates the need for computationally expensive hyper-parameter tuning, as the TR algorithm automatically determines the step size in each iteration. We demonstrate the capabilities, strengths, and limitations of the proposed APTS training method by performing a series of numerical experiments. The presented numerical study includes a comparison with widely used training methods such as SGD, Adam, LBFGS, and the standard TR method.

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Ken Trotti, Samuel A. Cruz Alegría, Alena Kopaničáková, Rolf Krause. 2023-12-21. Parallel Trust-Region Approaches in Neural Network Training: Beyond Traditional Methods. https://arxiv.org/abs/2312.13677

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