Tensor-Train Ranks for Matrices and Their Inverses. - Ivan V. Oseledets; Eugene E. Tyrtyshnikov; Nickolai L. Zamarashkin (Comput. Methods Appl. Math., 2011)

Construction and analysis of polar and concatenated polar codes: practical approach - Gregory Bonik; Sergei A. Goreinov; Nickolai L. Zamarashkin (CoRR, 2012)

A variant of list plus CRC concatenated polar code - Gregory Bonik; Sergei A. Goreinov; Nickolai L. Zamarashkin (CoRR, 2012)

Fast truncation of mode ranks for bilinear tensor operations. - Dmitry V. Savostyanov; Eugene E. Tyrtyshnikov; Nickolai L. Zamarashkin (Numer. Linear Algebra Appl., 2012)

On the Decay of the Elements of Inverse Triangular Toeplitz Matrices. - Neville J. Ford; Dmitry V. Savostyanov; Nickolai L. Zamarashkin (SIAM J. Matrix Anal. Appl., 2014)

GPU Acceleration of Dense Matrix and Block Operations for Lanczos Method for Systems over Large Prime Finite Field. - Nikolai L. Zamarashkin; Dmitry A. Zheltkov (RuSCDays, 2017)

Block Lanczos-Montgomery Method over Large Prime Fields with GPU Accelerated Dense Operations. - Nikolai L. Zamarashkin; Dmitry A. Zheltkov (RuSCDays, 2018)

On the Problem of Decoupling Multivariate Polynomials. - Stanislav Morozov; Dmitry A. Zheltkov; Nikolai L. Zamarashkin (LSSC, 2019)

Note: low-rank tensor train completion with side information based on Riemannian optimization. - Stanislav S. Budzinskiy; Nikolai L. Zamarashkin (CoRR, 2020)

On the algorithm of best approximation by low rank matrices in the Chebyshev norm. - Stanislav Morozov; Nikolai L. Zamarashkin; Eugene Tyrtyshnikov (CoRR, 2022)

Variational Bayesian inference for CP tensor completion with side information. - Stanislav S. Budzinskiy; Nikolai L. Zamarashkin (CoRR, 2022)

On the optimal rank-1 approximation of matrices in the Chebyshev norm. - Stanislav Morozov; Matvey Smirnov; Nikolai L. Zamarashkin (CoRR, 2022)

Theoretical Bounds for Noise Filtration using Low-Rank Tensor Approximations. - Sergey Petrov; Nikolai L. Zamarashkin (CoRR, 2023)

Tensor train completion: Local recovery guarantees via Riemannian optimization. - Stanislav S. Budzinskiy; Nikolai L. Zamarashkin (Numer. Linear Algebra Appl., 2023)