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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.
October 7, 2026
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The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
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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Fast Matrix Multiplication in Small Formats: Discovering New Schemes with an Open-Source Flip Graph Framework

2026.
Perminov Andrew Igorevich
An open-source C++ framework for discovering fast matrix multiplication schemes using the flip graph approach is presented. The framework supports multiple coefficient rings -- binary (Z2), modular ternary (Z3) and integer ternary (ZT={−1,0,1}) -- and implements both fixed-dimension and meta-dimensional search operators. Using efficient bit-level encoding of coefficient vectors and OpenMP parallelism, the tools enable large-scale exploration on commodity hardware. The study covers 680 schemes ranging from (2×2×2) to (16×16×16), with 276 schemes now in ZT coefficients and 117 in integer coefficients. With this framework, the multiplicative complexity (rank) is improved for 79 matrix multiplication schemes. Notably, a new 4×4×10 scheme requiring only 115 multiplications is discovered, achieving ω≈2.80478 and beating Strassen's exponent for this specific size. Additionally, 93 schemes are rediscovered in ternary coefficients that were previously known only over rationals or integers, and 68 schemes in integer coefficients that previously required fractions. All tools and discovered schemes are made publicly available to enable reproducible research.
Language: English
DOI
Text on another site
Keywords: тензорный рангtensor rankбыстрое матричное умножениефлип графfast matrix multiplicationflip graphternary integer coefficient setтернарное множество коэффициентов
Similar publications
Fast Matrix Multiplication via Ternary Meta Flip Graphs
Perminov Andrew Igorevich, / Series Computer Science "arxiv.org". 2025.
Matrix multiplication optimization remains a fundamental challenge in computational mathematics. This work introduces a novel approach that discovers matrix multiplication schemes whose coefficients are restricted to the set {−1,0,1} (denoted ZT), minimizing naive additive complexity for efficient hardware implementation. The core of the method is a GPU-accelerated meta flip graph algorithm that maintains ternary safety ...
Added: September 14, 2026
A 58-Addition, Rank-23 Scheme for General 3 × 3 Matrix Multiplication
Perminov Andrew Igorevich, / Series Computer Science "arxiv.org". 2025.
This paper presents a new state-of-the-art algorithm for exact 3×3 matrix multiplication over general non-commutative rings, achieving a rank-23 scheme with only 58 scalar additions. This improves the previous best additive complexity of 60 additions without a change of basis. The result was discovered through an automated search combining ternary-restricted flip-graph exploration with greedy intersection ...
Added: September 14, 2026
Parallel Heuristic Exploration for Additive Complexity Reduction in Fast Matrix Multiplication
Perminov Andrew Igorevich, / Series Computer Science "arxiv.org". 2025.
This paper presents a parallel random-search method for reducing additive complexity in fast matrix multiplication algorithms with ternary coefficients {−1,0,1}. The approach replaces expensive exact evaluation with fast heuristic scoring, including the new Greedy-Intersections strategy. The method runs many independent common subexpression elimination processes in parallel, exploring the search space through random pair substitutions and ...
Added: September 14, 2026
Meta Flip Graph meets Serendipitous Product: new Fast Matrix Multiplication results
Perminov Andrew Igorevich, / Series Computer Science "arxiv.org". 2026.
This paper presents new results for fast matrix multiplication in small formats obtained by combining the meta flip graph framework with the serendipitous product construction. The framework has been extended to support all 680 rectangular formats with dimensions up to 16×16×16. Compared to the previous state of the art, ranks are improved for 207 formats. ...
Added: September 14, 2026
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
Kodryan M., Kropotov D., Vetrov D., , in: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Volume 206Vol. 206.: Valencia: PMLR, 2023. P. 3718–3732.
Tensor decomposition methods have proven effective in various applications, including compression and acceleration of neural networks. At the same time, the problem of determining optimal decomposition ranks, which present the crucial parameter controlling the compressionaccuracy trade-off, is still acute. In this paper, we introduce MARS - a new efficient method for the automatic selection of ...
Added: June 9, 2023
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
Kodryan M., Kropotov D., Vetrov D., / Series QTNML 2020 "First Workshop on Quantum Tensor Networks in Machine Learning, NeurIPS 2020". 2020.
Tensor decomposition methods have recently proven to be efficient for compressing and accelerating neural networks. However, the problem of optimal decomposition structure determination is still not well studied while being quite important. Specifically, decomposition ranks present the crucial parameter controlling the compression-accuracy trade-off. In this paper, we introduce MARS - a new efficient method for ...
Added: February 5, 2021
Counterexamples to Strassen's direct sum conjecture
Shitov Y., Acta Mathematica 2019 Vol. 222 No. 2 P. 363–379
The rank of tensors is not additive with respect to the direct sum. ...
Added: November 22, 2020
Dense families of modular curves, prime numbers and uniform symmetric tensor rank of multiplication in certain finite fields
Zykin A. I., Ballet S., Designs, Codes and Cryptography 2019 Vol. 87 P. 517–525
We obtain new uniform bounds for the symmetric tensor rank of multiplication in finite extensions of any finite field F_p or F_{p^2} where p denotes a prime number ≥5. In this aim, we use the symmetric Chudnovsky-type generalized algorithm applied on sufficiently dense families of modular curves defined over F_{p_2} attaining the Drinfeld–Vladuts bound and on the descent of these families to ...
Added: May 12, 2020
On the rank of a Latin tensor
Shitov Y., Linear Algebra and its Applications 2018 Vol. 544 P. 299–305
An n×n matrix A is called permutative if the rows of A are distinct permutations of a family of n distinct elements. For all n⩾3, we show that the minimal rank of a non-negative permutative matrix equals 3. The minimal rank of a generic permutative n×n matrix equals the smallest integer r such that r!⩾n. ...
Added: January 30, 2019
A Counterexample to Comon's Conjecture
Shitov Y., SIAM Journal on Applied Algebra and Geometry 2018 Vol. 2 No. 3 P. 428–443
The rank and symmetric rank of a symmetric tensor may differ. ...
Added: September 26, 2018
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