In the paper https://arxiv.org/pdf/2212.11429.pdf https://arxiv.org/pdf/2212.11429.pdf In point 5.5 (page 57) Future work : " Promising areas of future work i

In the paper https://arxiv.org/pdf/2212.11429.pdf https://arxiv.org/pdf/2212.11429.pdf In point 5.5 (page 57) Future work : " Promising areas of future work include: • Mini-batch optimization. Both the theory and experiments in this chapter have been limited to full- batch optimization. However, the MM paradigm can be extended to mini-batch optimization (e.g., [54]). A universal mini-batch MM optimization algorithm may outperform Adam and AdaGrad on certain large-scale machine learning problems. " The current limits of this algorithm (point 2 page 56) is that the reverse mode bounding is not implemented yet (and related work didn't obtain tight bounds) so you can't yet apply it directly for function with huge number of input parameters, so for the time being the main usage is limited to few hyper-parameter optimizations.