Numerical analysis is one of the pillars, computer algebra being the other, of all computational algorithms. Accurate results of machine learning algorithms for classification, regression, and prediction are supported by theoretical features of numerical methods. The list of examples is overwhelming: principal component analysis based upon numerical linear algebra; optimization with Hopfield networks stemming from concepts rooted in dynamical systems; backpropagation that requires numerical optimizers; etc. On the other hand, research on computational intelligence techniques has led to advances in many numerical methods, with stochastic gradient descent being primus inter pares.

In this Special Issue, we aim at fostering the synergy between these two fields, by encouraging the analysis and design of numerical methods for, in, and from machine learning algorithms. We welcome contributions that highlight satisfactory learning results as soundly based on numerical foundations, as well as ground-breaking numerical methods that provide the basis for efficient practical algorithms, at least at the proof-of-concept stage.

The scope of the issue is deliberately broad, including but not limited to numerical techniques from linear algebra, dynamical systems, kernel methods, optimization, spectral methods, and stochastic formulations, as well as algorithms within neural networks, support vector machines, recurrent networks, and clustering methods.

Mathematics is a ISI-indexed (Q1, IF=1.747) journal.

Miguel Atencia
Daniela Danciu
Guest Editors