  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.
