## Abstract

We present a new type of optimization algorithms, adapted for neural network training. These algorithms are based upon sequential operator splitting technique for some associated dynamical systems. Furthermore, we investigate through numerical simulations the empirical rate of convergence of these iterative schemes toward a local minimum of the loss function, with some suitable choices of the underlying hyper parameters. We validate the convergence of these optimizers using the results of the accuracy and of the loss function on the MNIST,MNIST-Fashion and CIFAR 10 classification datasets.

## Authors

**Cristian Daniel Alecsa**

Department of Mathematics Babes-Bolyai University, Cluj-Napoca, Romania

Tiberiu Popoviciu Institute of Numerical Analysis Romanian Academy Cluj-Napoca, Romania

**Titus Pinta**

Department of Mathematics, Babes-Bolyai University, Cluj-Napoca, Romania

**Imre Boros**

Department of Mathematics Babes-Bolyai University Cluj-Napoca, Romania

Tiberiu Popoviciu Institute of Numerical Analysis Romanian Academy Cluj-Napoca, Romania

## Keywords

unconstrained optimization problems; splitting; neural network training;

## Paper coordinates

Cristian-Daniel Alecsa, Titus Pinta, Imre Boros, *New optimization algorithms for neural network training using operator splitting techniques, https://arxiv.org/pdf/1904.12952.pdf*

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## References

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