报告题目：Theory and Applications of Tensor Decompositions

报告人：Evgeny Tyrtyshnikov院士，Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences; Lomonosov Moscow State University, Russia

报告时间：2026年7月17日 09:00-10:00

报告地点：星空体育106会议室

报告摘要：Tensor decompositions become a very popular tool for modelling data in many application problems. We discuss some still open issues about the rank-bounded sets for the canonical polyadic decomposition and new developments of cross-approximation approach to optimization problems with the tensor-train model.

报告人简介：Evgeny Tyrtyshnikov is an academician of the Russian Academy of Sciences (elected in 2016), the Chairman of the National Committee for Industrial and Applied Mathematics. He is a leading researcher at the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. Professor and Chairman at the Lomonosov Moscow State University (since 2004). Best teacher of the year award at Lomonosov Moscow State University (2014). Laureate of the Sber Scientific Prize in the nomination “Digital Universe” (2023). Yangtze Professor at the Shenzhen-Moscow-Beijing University (since November 2024). Lingnan Fellow in the Lingnan University Institute for Advanced Study at Hong Kong.

Author of 8 books and more than 120 papers. Several collaborations with industry (Cray Research, Baker Hughes, Morgan Stanley, Huawei and others). Editor-in-chief of the Journal of Computational Mathematics and Mathematical Physics. Member of editorial boards of several journals: Calcolo, Sbornik Mathematics, Journal of Numerical Mathematics, Russian Journal of Numerical Analysis and Mathematical Modelling, Siberian Journal of Numerical Mathematics, Lobachevsky Journal of Mathematics. His research interests include multi-dimensional problems, structured matrices, asymptotic matrix analysis, spectral distributions, tensor decompositions and nonlinear approximations in linear algebra and numerical analysis.