Special issue in the "Boletín de la Sociedad Matemática Mexicana" is dedicated to modern trends at the intersection of asymptotic analysis, optimization, and machine learning. The international team of guest editors includes Pavel Naumkin, Mikhail Karapetyants, Nikolaos Pallikarakis, and Stefano Serra-Capizzano (Editor-in-Chief: Vladislav Kravchenko). See: https://link.springer.com/collections/iiiibdefbc .

The special issue addresses current issues arising at the intersection of asymptotic analysis, optimization theory, and modern machine learning methods. The journal focuses on both fundamental mathematical results and applied research related to artificial intelligence and big data processing.

We invite papers in the following areas (including, but not limited to): spectral asymptotics and eigenvalue distributions of large matrices; asymptotic analysis of optimization problems (including nonsmooth, convex, and nonconvex ones), as well as gradient methods; theoretical foundations of deep and reinforcement learning; stochastic optimization and its applications; and numerical methods for partial differential equations. Papers that combine classical methods of mathematical analysis with modern problems of machine learning and artificial intelligence are especially welcome.

The deadline for submission is December 31, 2026. Detailed information is available on the journal website: https://link.springer.com/collections/iiiibdefbc

Note that two special issues dedicated to AI foundations and applications have been previously launched as well. One in the Journal of Mathematical Sciences (Springer Nature, https://link.springer.com/collections/abceecbifg ), focuses on the mathematical foundations of artificial intelligence, while the second, in Mathematical Methods in the Applied Sciences (Wiley, https://onlinelibrary.wiley.com/page/journal/10991476/call-for-papers/si-2025-001556), covers broader applications of AI.