Yurii E. Nesterov - researchr alias

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2024

Gradient regularization of Newton method with Bregman distancesNikita Doikov, Yurii E. Nesterov. mp, 204(1):1-25, March 2024. [doi]

Super-Universal Regularized Newton MethodNikita Doikov, Konstantin Mishchenko, Yurii E. Nesterov. siamjo, 34(1):27-56, March 2024. [doi]

2023

Affine-invariant contracting-point methods for Convex OptimizationNikita Doikov, Yurii E. Nesterov. mp, 198(1):115-137, March 2023. [doi]

Adaptive Third-Order Methods for Composite Convex OptimizationGeovani Nunes Grapiglia, Yurii E. Nesterov. siamjo, 33(3):1855-1883, September 2023. [doi]

Conic optimization-based algorithms for nonnegative matrix factorizationValentin Leplat, Yurii E. Nesterov, Nicolas Gillis, François Glineur. oms, 38(4):837-859, July 2023. [doi]

Inexact accelerated high-order proximal-point methodsYurii E. Nesterov. mp, 197(1):1-26, January 2023. [doi]

Subgradient ellipsoid method for nonsmooth convex problemsAnton Rodomanov, Yurii E. Nesterov. mp, 199(1):305-341, May 2023. [doi]

2022

Efficient numerical methods to solve sparse linear equations with application to PageRankAnton Anikin 0002, Alexander V. Gasnikov, Alexander Gornov, Dmitry Kamzolov, Yury Maximov, Yurii E. Nesterov. oms, 37(3):907-935, 2022. [doi]

Discrete Choice Prox-Functions on the SimplexDavid Müller 0007, Yurii E. Nesterov, Vladimir Shikhman. mor, 47(1):485-507, 2022. [doi]

High-Order Optimization Methods for Fully Composite ProblemsNikita Doikov, Yurii E. Nesterov. siamjo, 32(3):2402-2427, September 2022. [doi]

Local convergence of tensor methodsNikita Doikov, Yurii E. Nesterov. mp, 193(1):315-336, 2022. [doi]

Tensor methods for finding approximate stationary points of convex functionsGeovani Nunes Grapiglia, Yurii E. Nesterov. oms, 37(2):605-638, 2022. [doi]

Gradient methods with memoryYurii E. Nesterov, Mihai I. Florea. oms, 37(3):936-953, 2022. [doi]

Inexact basic tensor methods for some classes of convex optimization problemsYurii E. Nesterov. oms, 37(3):878-906, 2022. [doi]

Rates of superlinear convergence for classical quasi-Newton methodsAnton Rodomanov, Yurii E. Nesterov. mp, 194(1):159-190, 2022. [doi]

2021

Minimizing Uniformly Convex Functions by Cubic Regularization of Newton MethodNikita Doikov, Yurii E. Nesterov. jota, 189(1):317-339, 2021. [doi]

On inexact solution of auxiliary problems in tensor methods for convex optimizationGeovani Nunes Grapiglia, Yurii E. Nesterov. oms, 36(1):145-170, 2021. [doi]

Inexact High-Order Proximal-Point Methods with Auxiliary Search ProcedureYurii E. Nesterov. siamjo, 31(4):2807-2828, 2021. [doi]

Superfast Second-Order Methods for Unconstrained Convex OptimizationYurii E. Nesterov. jota, 191(1):1-30, 2021. [doi]

Implementable tensor methods in unconstrained convex optimizationYurii E. Nesterov. mp, 186(1):157-183, 2021. [doi]

Primal-dual accelerated gradient methods with small-dimensional relaxation oracleYurii E. Nesterov, Alexander V. Gasnikov, Sergey Guminov, Pavel E. Dvurechensky. oms, 36(4):773-810, 2021. [doi]

Greedy Quasi-Newton Methods with Explicit Superlinear ConvergenceAnton Rodomanov, Yurii E. Nesterov. siamjo, 31(1):785-811, 2021. [doi]

New Results on Superlinear Convergence of Classical Quasi-Newton MethodsAnton Rodomanov, Yurii E. Nesterov. jota, 188(3):744-769, 2021. [doi]

2020

Contracting Proximal Methods for Smooth Convex OptimizationNikita Doikov, Yurii E. Nesterov. siamjo, 30(4):3146-3169, 2020. [doi]

Convex optimization based on global lower second-order modelsNikita Doikov, Yurii E. Nesterov. nips 2020: [doi]

Inexact Tensor Methods with Dynamic AccuraciesNikita Doikov, Yurii E. Nesterov. icml 2020: 2577-2586 [doi]

Tensor Methods for Minimizing Convex Functions with Hölder Continuous Higher-Order DerivativesGeovani Nunes Grapiglia, Yurii E. Nesterov. siamjo, 30(4):2750-2779, 2020. [doi]

Stochastic Subspace Cubic {NFilip Hanzely, Nikita Doikov, Yurii E. Nesterov, Peter Richtárik. icml 2020: 4027-4038 [doi]

Computing Closest Stable Nonnegative MatrixYurii E. Nesterov, Vladimir Yu. Protasov. siammax, 41(1):1-28, 2020. [doi]

Soft clustering by convex electoral modelYurii E. Nesterov. soco, 24(23):17609-17620, 2020. [doi]

2013

On first-order algorithms for l1/nuclear norm minimizationYurii E. Nesterov, Arkadi Nemirovski. actanum, 22:509-575, 2013. [doi]

Nearest stable system using successive convex approximationsFrancois-Xavier Orbandexivry, Yurii E. Nesterov, Paul Van Dooren. automatica, 49(5):1195-1203, 2013. [doi]

2011

Indirect reciprocity through gossiping can lead to cooperative clustersVincent A. Traag, Paul Van Dooren, Yurii E. Nesterov. ieeealife 2011: 154-161 [doi]

2010

Exponential Ranking: Taking into Account Negative LinksVincent A. Traag, Yurii E. Nesterov, Paul Van Dooren. socinfo 2010: 192-202 [doi]

2008

Optimizing the Coupling Between Two Isometric Projections of MatricesCatherine Fraikin, Yurii E. Nesterov, Paul Van Dooren. siammax, 30(1):324-345, 2008. [doi]

2003

Optimization Problems over Positive Pseudopolynomial MatricesY. Genin, Y. Hachez, Yurii E. Nesterov, Paul Van Dooren. siammax, 25(1):57-79, 2003. [doi]

2002

Positivity and Linear Matrix InequalitiesYvan Hachez, Yurii E. Nesterov, Radu Stefan. ejcon, 8(3):275-298, 2002. [doi]

1999

Positive transfer functions and convex optimizationYves V. Genin, Yurii E. Nesterov, Paul Van Dooren. eucc 1999: 242-246 [doi]

The analytic center of LMI's and Riccati equationsYves V. Genin, Yurii E. Nesterov, Paul Van Dooren. eucc 1999: 3483-3487 [doi]

1998

Primal-Dual Interior-Point Methods for Self-Scaled ConesYurii E. Nesterov, Michael J. Todd. siamjo, 8(2):324-364, 1998. [doi]

1997

Self-Scaled Barriers and Interior-Point Methods for Convex ProgrammingYurii E. Nesterov, Michael J. Todd. mor, 22(1):1-42, 1997. [doi]

1994

Interior-point polynomial algorithms in convex programmingYurii E. Nesterov, Arkadii Nemirovskii. Volume 13 of Siam studies in applied mathematics, SIAM, 1994. [doi]

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