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October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
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International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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Lower and upper bounds for the largest Lyapunov exponent of matrices

Linear Algebra and its Applications. 2013. Vol. 438. No. 11. P. 4448–4468.
Protasov V., Jungers R.

We introduce a new approach to evaluate the largest Lyapunov exponent of a family of nonnegative matrices. The method is based on using special positive homogeneous functionals on , which gives iterative lower and upper bounds for the Lyapunov exponent. They improve previously known bounds and converge to the real value. The rate of convergence is estimated and the efficiency of the algorithm is demonstrated on several problems from applications (in functional analysis, combinatorics, and language theory) and on numerical examples with randomly generated matrices. The method computes the Lyapunov exponent with a prescribed accuracy in relatively high dimensions (up to 60). We generalize this approach to all matrices, not necessarily nonnegative, derive a new universal upper bound for the Lyapunov exponent, and show that a potential similar lower bound does not exist in general.

Priority areas: IT and mathematics mathematics engineering science
Language: English
DOI
Text on another site
Keywords: algorithmLyapunov exponentOseledets Multiplicative Ergodic Theoremconvex optimization
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