# New optimization algorithms for neural network training using operator splitting techniques

## TL;DR

This paper introduces novel optimization algorithms for neural network training based on operator splitting techniques, demonstrating their effectiveness through empirical convergence analysis and validation on standard datasets.

## Contribution

It proposes a new class of optimization algorithms using operator splitting for neural networks, with empirical validation of convergence and performance.

## Key findings

- Algorithms converge towards local minima
- Validated on MNIST, MNIST-Fashion, CIFAR-10 datasets
- Show improved or comparable accuracy and loss reduction

## Abstract

In the following paper 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.