Publications Peer reviewed articles (21) On inexact solution of auxiliary problems in tensor methods for convex optimization Author(s): G.N. Grapiglia, Yu. Nesterov Published in: Optimization Methods and Software, Issue 36(1), 2021, Page(s) 145 - 170, ISSN 1055-6788 Publisher: Taylor & Francis DOI: 10.1080/10556788.2020.1731749 Tensor Methods for Minimizing Convex Functions with Hölder Continuous Higher-Order Derivatives Author(s): G. N. Grapiglia, Yu. Nesterov Published in: SIAM Journal on Optimization, Issue 30/4, 2020, Page(s) 2750-2779, ISSN 1052-6234 Publisher: Society for Industrial and Applied Mathematics DOI: 10.1137/19m1259432 Superfast second-order methods for unconstrained convex optimization Author(s): Yurii Nesterov Published in: Journal of Optimization Theory and Applications, Issue 191, 2021, Page(s) 1-30, ISSN 0022-3239 Publisher: Kluwer Academic/Plenum Publishers DOI: 10.1007/s10957-021-01930-y Inexact High-Order Proximal-Point Methods with Auxiliary Search Procedure Author(s): Yurii Nesterov Published in: SIAM Journal of Optimization, Issue 31(4), 2021, Page(s) 2807-2828, ISSN 1052-6234 Publisher: Society for Industrial and Applied Mathematics DOI: 10.1137/20m134705x Gradient methods with memory Author(s): Yurii Nesterov, Mihai I. Florea Published in: Optimization Methods and Software, Issue Online First, 2021, Page(s) 1-18, ISSN 1055-6788 Publisher: Taylor & Francis DOI: 10.1080/10556788.2020.1858831 Contracting Proximal Methods for Smooth Convex Optimization Author(s): Nikita Doikov, Yurii Nesterov Published in: SIAM Journal on Optimization, Issue 30/4, 2020, Page(s) 3146-3169, ISSN 1052-6234 Publisher: Society for Industrial and Applied Mathematics DOI: 10.1137/19m130769x Affine-invariant contracting-point methods for Convex Optimization Author(s): Nikita Doikov; Yurii Nesterov Published in: Mathematical Programming, (2022), Issue 198(1), 2023, Page(s) 115–137, ISSN 0025-5610 Publisher: Springer Verlag DOI: 10.1007/s10107-021-01761-9 Rates of superlinear convergence for classical quasi-Newton methods Author(s): Anton Rodomanov, Yurii Nesterov Published in: Mathematical Programming, Issue 188(3), 2021, Page(s) 744-769, ISSN 0025-5610 Publisher: Springer Verlag DOI: 10.1007/s10107-021-01622-5 New Results on Superlinear Convergence of Classical Quasi-Newton Methods Author(s): Anton Rodomanov, Yurii Nesterov Published in: Journal of Optimization Theory and Applications, Issue 188/3, 2021, Page(s) 744-769, ISSN 0022-3239 Publisher: Kluwer Academic/Plenum Publishers DOI: 10.1007/s10957-020-01805-8 Local convergence of tensor methods Author(s): Nikita Doikov, Yurii Nesterov Published in: Mathematical Programming, Issue Online First, 2021, ISSN 0025-5610 Publisher: Springer Verlag DOI: 10.1007/s10107-020-01606-x Inexact basic tensor methods for some classes of convex optimization problems Author(s): Yurii Nesterov Published in: Optimization Methods and Software, Issue Online First, 2020, Page(s) 1-29, ISSN 1055-6788 Publisher: Taylor & Francis DOI: 10.1080/10556788.2020.1854252 Smoothness Parameter of Power of Euclidean Norm Author(s): Anton Rodomanov, Yurii Nesterov Published in: Journal of Optimization Theory and Applications, 2020, ISSN 0022-3239 Publisher: Kluwer Academic/Plenum Publishers DOI: 10.1007/s10957-020-01653-6 Implementable tensor methods in unconstrained convex optimization Author(s): Yurii Nesterov Published in: Mathematical Programming, 2019, ISSN 0025-5610 Publisher: Springer Verlag DOI: 10.1007/s10107-019-01449-1 Tensor methods for finding approximate stationary points of convex functions Author(s): G.N. Grapiglia, Yu. Nesterov Published in: Optimization Methods and Software, Issue Online First, 2021, ISSN 1055-6788 Publisher: Taylor & Francis DOI: 10.1080/10556788.2020.1818082 Greedy Quasi-Newton Methods with Explicit Superlinear Convergence Author(s): Anton Rodomanov, Yurii Nesterov Published in: SIAM Journal on Optimization, Issue 31(1), 2021, Page(s) 785–811, ISSN 1052-6234 Publisher: Society for Industrial and Applied Mathematics DOI: 10.1137/20m1320651 High-Order Optimization Methods for Fully Composite Problems Author(s): Nikita Doikov; Yurii Nesterov Published in: SIAM Journal on Optimization, Issue 32(3), 2022, Page(s) 2402-2427, ISSN 1052-6234 Publisher: Society for Industrial and Applied Mathematics DOI: 10.1137/21m1410063 Gradient regularization of Newton method with Bregman distances Author(s): Nikita Doikov; Yurii Nesterov Published in: Mathematical Programming, Issue Online First, 2023, ISSN 0025-5610 Publisher: Springer Verlag DOI: 10.1007/s10107-023-01943-7 Inexact accelerated high-order proximal-point methods Author(s): Yurii Nesterov Published in: Mathematical Programming, Issue 197, 2023, Page(s) 1-26, ISSN 0025-5610 Publisher: Springer Verlag DOI: 10.1007/s10107-021-01727-x Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method Author(s): Nikita Doikov, Yurii Nesterov Published in: Journal of Optimization Theory and Applications, Issue 189, 2021, Page(s) 317-339, ISSN 0022-3239 Publisher: Kluwer Academic/Plenum Publishers DOI: 10.1007/s10957-021-01838-7 Subgradient ellipsoid method for nonsmooth convex problems Author(s): Anton Rodomanov; Yurii Nesterov Published in: Mathematical Programming, Issue 199, 2023, Page(s) 305-341, ISSN 0025-5610 Publisher: Springer Verlag DOI: 10.1007/s10107-022-01833-4 Set-Limited Functions and Polynomial-Time Interior-Point Methods Author(s): Nesterov Yurii Published in: Journal of Optimization Theory and Applications, Issue Online First, 2023, ISSN 0022-3239 Publisher: Kluwer Academic/Plenum Publishers DOI: 10.1007/s10957-023-02163-x Other (1) Complexity and Simplicity of Optimization Problems Author(s): Yurii Nesterov Published in: The Project Repository Journal, Issue 6, 2020, Page(s) 130-131, ISSN 2632-4067 Publisher: European Dissemination Media Conference proceedings (3) Convex optimization based on global lower second-order models Author(s): Nikita Doikov, Yurii Nesterov Published in: Advances in Neural Information Processing Systems (NeurIPS), Issue 33, 2020, Page(s) 16546-16556 Publisher: Neural Information Processing Systems Inexact Tensor Methods with Dynamic Accuracies Author(s): Nikita Doikov, Yurii Nesterov Published in: Proceedings of the 37th International Conference on Machine Learning (ICML), Issue 119, 2020, Page(s) 2577-2586 Publisher: ML Research Press Stochastic Subspace Cubic Newton Method Author(s): Filip Hanzely, Nikita Doikov, Peter Richtárik, Yurii Nesterov Published in: Proceedings of the 37th International Conference on Machine Learning, Issue 119, 2020, Page(s) 4027-4038 Publisher: ML Research Press Non-peer reviewed articles (1) Soft clustering by convex electoral model Author(s): Yurii Nesterov Published in: Soft Computing, Issue 24/23, 2020, Page(s) 17609-17620, ISSN 1432-7643 Publisher: Springer Verlag DOI: 10.1007/s00500-020-05148-4 Searching for OpenAIRE data... 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