| Putting MRFs on a tensor train A Novikov, A Rodomanov, A Osokin, D Vetrov International Conference on Machine Learning, 811-819, 2014 | 52 | 2014 |
| Greedy quasi-Newton methods with explicit superlinear convergence A Rodomanov, Y Nesterov SIAM Journal on Optimization 31 (1), 785-811, 2021 | 48 | 2021 |
| Rates of superlinear convergence for classical quasi-Newton methods A Rodomanov, Y Nesterov Mathematical Programming, 1-32, 2021 | 45 | 2021 |
| New Results on Superlinear Convergence of Classical Quasi-Newton Methods A Rodomanov, Y Nesterov Journal of Optimization Theory and Applications 188, 744-769, 2021 | 44 | 2021 |
| A superlinearly-convergent proximal Newton-type method for the optimization of finite sums A Rodomanov, D Kropotov International Conference on Machine Learning, 2597-2605, 2016 | 43 | 2016 |
| Primal-dual method for searching equilibrium in hierarchical congestion population games P Dvurechensky, A Gasnikov, E Gasnikova, S Matsievsky, A Rodomanov, ... arXiv preprint arXiv:1606.08988, 2016 | 38 | 2016 |
| A randomized coordinate descent method with volume sampling A Rodomanov, D Kropotov SIAM Journal on Optimization 30 (3), 1878-1904, 2020 | 9 | 2020 |
| Smoothness parameter of power of Euclidean norm A Rodomanov, Y Nesterov Journal of Optimization Theory and Applications 185, 303-326, 2020 | 8 | 2020 |
| Subgradient ellipsoid method for nonsmooth convex problems A Rodomanov, Y Nesterov Mathematical Programming 199 (1-2), 305-341, 2023 | 2 | 2023 |
| Quasi-Newton Methods with Provable Efficiency Guarantees A Rodomanov PhD thesis, UCL-Université Catholique de Louvain, 2022 | 1 | 2022 |
| Polynomial Preconditioning for Gradient Methods N Doikov, A Rodomanov arXiv preprint arXiv:2301.13194, 2023 | | 2023 |
| Randomized Minimization of Eigenvalue Functions Y Nesterov, A Rodomanov arXiv preprint arXiv:2301.08352, 2023 | | 2023 |
| Linear Coupling of Gradient and Mirror Descent: Version for Composite Functions with Adaptive Estimation of the Lipschitz Constant A Rodomanov | | 2016 |
