Conference

«Linear and Non-Linear Approximation Methods:
Interrelation and Applications»

The aim of the conference is to develop modern approximation theory as a mathematical foundation for solving the problem of analyzing and processing large volumes of data. This problem is extremely acute for modern science and technology. There are a number of modern methods that have proven very successful in practice. At the same time, a theoretical justification for the success of these methods is absent.

To conduct world-class theoretical research in the highly relevant direction of big data processing, researchers use recent breakthrough achievements in mathematical approaches to this problem (image compression, sparse representations, greedy approximations, learning theory). In doing so, both classical linear methods and modern non-linear approximation methods are widely used.

The conference is connected with the following actively developing fields of research: linear and non-linear methods of function recovery from samples, approximation in spaces of smooth functions, numerical integration, convex optimization, orthogonal series, greedy algorithms, geometric approximation theory in Banach spaces, quantized approximations, learning theory, and compressed sensing.

The conference is held with the financial support of the Ministry of Science and Higher Education within the framework of the program of the Moscow Center for Fundamental and Applied Mathematics under Agreement No. 075-15-2025-345.