[31] Savchuk, O.S., Alkousa, M.S., Shushko, A.S., Vyguzov, A.A., Stonyakin, F.S., Pasechnyuk, D.A., Gasnikov, A.V. Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems. (2024) URL https://arxiv.org/abs/2411.16743

[30] Kamzolov, D.I., Pasechnyuk, D.A., Agafonov, A., Gasnikov, A.V., Takáč, M. OPTAMI: Global Superlinear Convergence of High-order Methods. (2024) URL https://arxiv.org/abs/2410.04083

[29] Pasechnyuk, D.A., Dvurechensky, P., Uribe, C.A., Gasnikov, A.V. Decentralized convex optimisation with probability-proportional-to-size quantization. (2024) URL TBA

[28] Pasechnyuk, D.A., Lobanov, A., Gasnikov, A. Upper bounds on the maximum admissible level of noise in zeroth-order optimisation. (2023) URL https://arxiv.org/abs/2306.16371

[27] Pasechnyuk, D.A., Gasnikov, A., Takáč, M. Convergence analysis of stochastic gradient descent with adaptive preconditioning for non-convex and convex functions. (2023) URL https://arxiv.org/abs/2308.14192

[26] Brilliantov, K., Pavutnitskiy, F.Yu., Pasechnyuk, D.A., Magai, G. Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formula. Proceedings of the 41st International Conference on Machine Learning, PMLR 235, 4542–4560 (2024) URL https://proceedings.mlr.press/v235/brilliantov24a.html

[25] Kubentayeva, M., Yarmoshik, D., Persiianov, M., Kroshnin, A., Kotliarova, E., Tupitsa, N., Pasechnyuk, D., Gasnikov, A., Shvetsov, V., Baryshev, L., Shurupov, A. Primal-Dual Gradient Methods for Searching Network Equilibria in Combined Models with Nested Choice Structure and Capacity Constraints. Computational Management Science: Network Analysis and Applications 21(1), 15 (2023) DOI 10.1007/s10287-023-00494-8

[24] Ablaev, S.S., Stonyakin, F.S., Alkousa, M.S., Pasechnyuk, D.A. Adaptive methods for variational inequalities with relatively smooth and relatively strongly monotone operators. Programming and Computer Software 49(6), 485–492 (2023) DOI 10.1134/S0361768823060026

[23] Aivazian, G.V., Stonyakin, F.S., Pasechnyuk, D.A., Alkousa, M.S., Raigorodskii, A.M., Baran I.V. Adaptive variant of the Frank–Wolfe algorithm for convex optimization problems. Programming and Computer Software 49(6), 493–504 (2023) DOI 10.1134/S0361768823060038

[22] Pasechnyuk, D.A., Persiianov, M., Dvurechensky, P., Gasnikov, A. Algorithms for Euclidean-regularised Optimal Transport. International Conference on Optimization and Applications. Lecture Notes in Computer Science 14395, 84–98. Springer, Cham (2023). DOI 10.1007/978-3-031-47859-8_7

[21] Pasechnyuk, D.A., Gornov, A.Yu. A randomised non-descent method for global optimisation. International Conference on Optimization and Applications. Communications in Computer and Information Science 1913, 3–14. Springer, Cham (2023). DOI 10.1007/978-3-031-48751-4_1

[20] Gladin, E.L., Kuruzov, I.A., Pasechnyuk, D.A., Stonyakin, F.S., Alkousa, M.S., Gasnikov, A.V. Solving strongly convex-concave composite saddle point problems with a small dimension of one of the variables. Matematicheskii Sbornik 214(3), 3–53 (2023) DOI 10.4213/sm9700

[19] Pasechnyuk, D., Prazdnichnykh, A., Evtikhiev, M., Bryksin, T. Judging Adam: Studying Optimizer Performance On ML4SE Tasks. IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results, 117–122 (2023) DOI 10.1109/ICSE-NIER58687.2023.00027

[18] Beznosikov, A.N., Gasnikov, A.V., Zainullina, K.E., Maslovskiy, A.Yu., Pasechnyuk, D.A. A Unified Analysis of Variational Inequality Methods: Variance Reduction, Sampling, Quantization and Coordinate Descent. Computational Mathematics and Mathematical Physics 63(2), 189–217 (2023) DOI 10.1134/S0965542523020045

[17] Pasechnyuk, D.A., Gasnikov, A., Takáč, M. Effects of momentum scaling for SGD. NeurIPS Workshop HOO (2022) URL https://order-up-ml.github.io/papers/17.pdf

[16] Hanzely, S., Kamzolov, D., Pasechnyuk, D., Gasnikov, A., Richtárik, P., Takáč, M. A Damped Newton Method Achieves Global O(1/k²) and Local Quadratic Convergence Rate. Advances in Neural Information Processing Systems 35, 25320–25334 (2022) URL https://proceedings.neurips.cc/paper_files/paper/2022/hash/a1f0c0cd6caaa4863af5f12608edf63e-Abstract-Conference.html

[15] Ivanova, A., Dvurechensky, P., Vorontsova, E., Pasechnyuk, D., Gasnikov, A., Dvinskikh, D., Tyurin, A. Oracle Complexity Separation in Convex Optimization. Journal of Optimization Theory and Applications (2022). DOI 10.1007/s10957-022-02038-7

[14] Alpatov, A.V., Peters, E.A., Pasechnyuk, D.A., Raigorodskii, A.M. Stochastic optimization in digital pre-distortion of the signal. Computer Research and Modeling 14(2), 399–416 (2022) DOI 10.20537/2076-7633-2022-14-2-399-416

[13] Pasechnyuk, D.A., Anikin, A.S., Matyukhin, V.V. Accelerated Proximal Envelopes: Application to the Coordinate Descent Method. Computational Mathematics and Mathematical Physics 62(2), 342–352 (2022) DOI 10.1134/S0965542522020038

[12] Pasechnyuk, D., Raigorodskii, A. Network utility maximization by updating individual transmission rates. International Conference on Optimization and Applications. Communications in Computer and Information Science 1514, 184–198. Springer, Cham (2021). DOI 10.1007/978-3-030-92711-0_13

[11] Pasechnyuk, D., Dvurechensky, P., Omelchenko, S., Gasnikov, A. Stochastic optimization for dynamic pricing. International Conference on Optimization and Applications. Communications in Computer and Information Science 1514, 82–94. Springer, Cham (2021). DOI 10.1007/978-3-030-92711-0_6

[10] Pasechnyuk, D., Matyukhin, V. On the Computational Efficiency of Catalyst Accelerated Coordinate Descent. International Conference on Mathematical Optimization Theory and Operations Research. Lecture Notes in Computer Science 12755, 176–191. Springer, Cham (2021). DOI 10.1007/978-3-030-77876-7_12

[9] Maslovskiy, A., Pasechnyuk, D., Gasnikov, A., Anikin, A., Rogozin, A., Gornov, A., Vorobyev, A., Antonov, L., Vlasov, R., Nikolaeva, A., Yanitskiy, E., Begicheva, M. Non-convex optimization in digital pre-distortion of the signal. International Conference on Mathematical Optimization Theory and Operations Research. Communications in Computer and Information Science 1476, 54–70. Springer, Cham (2021). DOI 10.1007/978-3-030-86433-0_4

[8] Gasnikov, A.V., Dvinskikh, D.M., Dvurechensky, P.E., Kamzolov, D.I., Matyukhin, V.V., Pasechnyuk, D.A., Tupitsa, N.K., Chernov, A.V. Accelerated meta-algorithm for convex optimization. Computational Mathematics and Mathematical Physics 61(1), 17–28 (2021) DOI 10.1134/S096554252101005X

[7] Ivanova, A., Pasechnyuk, D., Grishchenko, D., Shulgin, E., Gasnikov, A., Matyukhin, V. Adaptive catalyst for smooth convex optimization. International Conference on Optimization and Applications. Lecture Notes in Computer Science 13078, 20–37. Springer, Cham (2021). DOI 10.1007/978-3-030-91059-4_2

[6] Ivanova, A.S., Pasechnyuk, D.A., Dvurechensky, P.E., Gasnikov, A.V., Vorontsova, E.A. Numerical methods for the resource allocation problem in networks. Computational Mathematics and Mathematical Physics 61(2), 312–345 (2021) DOI 10.1134/S0965542521020135

[5] Stonyakin, F., Tyurin, A., Gasnikov, A., Dvurechensky, P., Agafonov, A., Dvinskikh, D., Alkousa, M., Pasechnyuk, D., Artamonov S., Piskunova, V. Inexact model: A framework for optimization and variational inequalities. Optimization Methods and Software, 1–47 (2021). DOI 10.1080/10556788.2021.1924714

[4] Ivanova, A., Stonyakin, F., Pasechnyuk, D., Vorontsova, E., Gasnikov, A. Adaptive Mirror Descent for the Network Utility Maximization Problem. IFAC-PapersOnLine 53(2), 7851–7856 (2020). DOI 10.1016/j.ifacol.2020.12.1958

[3] Pasechnyuk, D.A., Stonyakin, F.S. One method for minimization a convex Lipschitz-continuous function of two variables on a fixed square. Computer Research and Modeling 11(3), 379–395 (2019) DOI 10.20537/2076-7633-2019-11-3-379-395

[2] Stonyakin, F., Dvinskikh, D., Dvurechensky, P., Kroshnin, A., Kuznetsova, O., Agafonov, A., Gasnikov, A., Tyurin, A., Uribe, C., Pasechnyuk, D., Artamonov, S. Gradient methods for problems with inexact model of the objective. International Conference on Mathematical Optimization Theory and Operations Research. Lecture Notes in Computer Science 11548, 97–114. Springer, Cham (2019). DOI 10.1007/978-3-030-22629-9_8

[1] Pasechnyuk D.A. Scheduling strategies for resource allocation in a cellular base station. Proceedings of MIPT 11(2), 38–48 (2019) URL https://mipt.ru/upload/medialibrary/f16/4_trudy-mfti-_2_42_38_48.pdf