Greedy quasi-Newton methods with explicit superlinear convergence A Rodomanov, Y Nesterov
SIAM Journal on Optimization 31 (1), 785-811, 2021
66 2021 Rates of superlinear convergence for classical quasi-Newton methods A Rodomanov, Y Nesterov
Mathematical Programming, 1-32, 2022
58 2022 Putting MRFs on a tensor train A Novikov, A Rodomanov, A Osokin, D Vetrov
International Conference on Machine Learning, 811-819, 2014
56 2014 New Results on Superlinear Convergence of Classical Quasi-Newton Methods A Rodomanov, Y Nesterov
Journal of Optimization Theory and Applications 188, 744-769, 2021
54 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
48 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
37 2016 A randomized coordinate descent method with volume sampling A Rodomanov, D Kropotov
SIAM Journal on Optimization 30 (3), 1878-1904, 2020
14 2020 Smoothness parameter of power of Euclidean norm A Rodomanov, Y Nesterov
Journal of Optimization Theory and Applications 185, 303-326, 2020
11 2020 Subgradient ellipsoid method for nonsmooth convex problems A Rodomanov, Y Nesterov
Mathematical Programming 199 (1), 305-341, 2023
6 2023 Quasi-Newton methods with provable efficiency guarantees A Rodomanov
PhD thesis, UCL-Université Catholique de Louvain, 2022
3 2022 Universal Gradient Methods for Stochastic Convex Optimization A Rodomanov, A Kavis, Y Wu, K Antonakopoulos, V Cevher
arXiv preprint arXiv:2402.03210, 2024
2 2024 Stabilized proximal-point methods for federated optimization X Jiang, A Rodomanov, SU Stich
arXiv preprint arXiv:2407.07084, 2024
1 2024 Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction A Rodomanov, X Jiang, S Stich
arXiv preprint arXiv:2406.06398, 2024
1 2024 Global Complexity Analysis of BFGS A Rodomanov
arXiv preprint arXiv:2404.15051, 2024
1 2024 Federated Optimization with Doubly Regularized Drift Correction X Jiang, A Rodomanov, SU Stich
arXiv preprint arXiv:2404.08447, 2024
1 2024 Non-Convex Stochastic Composite Optimization with Polyak Momentum Y Gao, A Rodomanov, SU Stich
arXiv preprint arXiv:2403.02967, 2024
1 2024 Polynomial preconditioning for gradient methods N Doikov, A Rodomanov
International Conference on Machine Learning, 8162-8187, 2023
1 2023 Optimizing -Smooth Functions by Gradient Methods D Vankov, A Rodomanov, A Nedich, L Sankar, SU Stich
arXiv preprint arXiv:2410.10800, 2024
2024 Gradient Methods for Stochastic Optimization in Relative Scale Y Nesterov, A Rodomanov
arXiv preprint arXiv:2301.08352, 2023
2023