With the increasing availability of large datasets and the growing complexity of models, there is an increasing need for efficient optimization techniques that can handle the computational challenges associated with big data. Especially in the context of deep learning, where models can have millions of parameters and require large amounts of data to train, the choice of optimization algorithm can have a significant impact on the performance of the model. As to limit the computational footprint of such data-driven methods, the search for more efficient optimization techniques is an ongoing field of research. There will also be further advances in the methods to choose hyperparameters of model training to make the usage of numerical models easier and more efficient to use.

As the scope of this paper was limited to the study of a simple toy dataset with simple models, the results cannot be directly transferred to more complex models and datasets, and need to be researched accordingly. However, the general trends observed in this study can be expected to hold true for more complex models and datasets as well. The choice of optimization algorithm and cost function can have a significant impact on the performance of the model, and reducing the computational cost of training is an important consideration in the context of big data.