Fetching the paper…
Reading the bibliography…
In this paper, we present a new Hyperfast Second-Order Method with convergence rate $O(N^{-5})$ up to a logarithmic factor for the convex function with Lipshitz the third derivative.
Cubic regularization of newton method and its global performance
Yurii Nesterov and Boris T Polyak · 2006
Earlier work this paper cites.
Stochastic convex optimization
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2009
Earlier work this paper cites.
An accelerated hybrid proximal extragradient method for convex optimization and its implications to second-order methods
Renato DC Monteiro and Benar Fux Svaiter · 2013
Earlier work this paper cites.
Powers of tensors and fast matrix multiplication
François Le Gall · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Earlier work this paper cites.
Gradient sliding for composite optimization
Guanghui Lan · 2016
Earlier work this paper cites.
Accelerated gradient sliding for structured convex optimization
Guanghui Lan and Yuyuan Ouyang · 2016
Earlier work this paper cites.
Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models
Ernesto G Birgin, JL Gardenghi, José Mario Martínez, Sandra Augusta Santos, and Ph L Toint · 2017
Earlier work this paper cites.
Lower bounds for finding stationary points ii: first-order methods
Y Carmon, JC Duchi, O Hinder, and A Sidford · 2017
Earlier work this paper cites.
Alexander Gasnikov · 2017
Earlier work this paper cites.
Towards optimal running times for optimal transport
Jose Blanchet, Arun Jambulapati, Carson Kent, and Aaron Sidford · 2018
Earlier work this paper cites.
Introductory lectures on stochastic optimization
John C Duchi · 2018
Earlier work this paper cites.
Pavel Dvurechensky, Alexander Gasnikov, and Alexey Kroshnin · 2018
Earlier work this paper cites.
A hypothesis about the rate of global convergence for optimal methods (newton’s type) in smooth convex optimization
Alexander Vladimirovich Gasnikov and Dmitry A Kovalev · 2018
Earlier work this paper cites.
An optimal randomized incremental gradient method
Guanghui Lan and Yi Zhou · 2018
Earlier work this paper cites.
Lectures on convex optimization
Yurii Nesterov · 2018
Earlier work this paper cites.
Approximating optimal transport with linear programs
Kent Quanrud · 2018
Cited alongside, same era.
A note on inexact condition for cubic regularized newton’s method
Zhe Wang, Yi Zhou, Yingbin Liang, and Guanghui Lan · 2018
Cited alongside, same era.
Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Blake E Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, and Nati Srebro · 2018
Cited alongside, same era.
Complexity of highly parallel non-smooth convex optimization
Sébastien Bubeck, Qijia Jiang, Yin-Tat Lee, Yuanzhi Li, and Aaron Sidford · 2019
Cited alongside, same era.
Near-optimal method for highly smooth convex optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, and Aaron Sidford · 2019
Cited alongside, same era.
Sergey Guminov, Pavel Dvurechensky, Tupitsa Nazary, and Alexander Gasnikov · 2019
Later among the works it cites.
Adaptive catalyst for smooth convex optimization
Anastasiya Ivanova, Dmitry Grishchenko, Alexander Gasnikov, and Egor Shulgin · 2019
Later among the works it cites.
A direct tilde { \{ O } \} (1/epsilon) iteration parallel algorithm for optimal transport
Arun Jambulapati, Aaron Sidford, and Kevin Tian · 2019
Later among the works it cites.
An optimal high-order tensor method for convex optimization
Bo Jiang, Haoyue Wang, and Shuzhong Zhang · 2019
Later among the works it cites.
Solving linear programs with sqrt (rank) linear system solves
Yin Tat Lee and Aaron Sidford · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nikita Doikov and Yurii Nesterov · 2019
Cited alongside, same era.
Local convergence of tensor methods
Nikita Doikov and Yurii Nesterov · 2019
Cited alongside, same era.
Darina Dvinskikh and Alexander Gasnikov · 2019
Cited alongside, same era.
Near-optimal tensor methods for minimizing the gradient norm of convex function
Pavel Dvurechensky, Alexander Gasnikov, Petr Ostroukhov, César A Uribe, and Anastasiya Ivanova · 2019
Cited alongside, same era.
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate
Vitaly Feldman and Jan Vondrak · 2019
Cited alongside, same era.
Optimal tensor methods in smooth convex and uniformly convexoptimization
Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, and César A Uribe · 2019
Cited alongside, same era.
Near optimal methods for minimizing convex functions with lipschitz p p -th derivatives
Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, et al · 2019
Cited alongside, same era.
Later among the works it cites.
Efficient convex optimization with oracles
Yin Tat Lee, Aaron Sidford, and Santosh S Vempala · 2019
Later among the works it cites.
Implementable tensor methods in unconstrained convex optimization
Yurii Nesterov · 2019
Later among the works it cites.
Towards unified acceleration of high-order algorithms under hölder continuity and uniform convexity
Chaobing Song and Yi Ma · 2019
Later among the works it cites.
Cubic regularization with momentum for nonconvex optimization
Z Wang, Y Zhou, Y Liang, and G Lan · 2019
Later among the works it cites.
Accelerating rescaled gradient descent
Ashia Wilson, Lester Mackey, and Andre Wibisono · 2019
Later among the works it cites.
Statistically preconditioned accelerated gradient method for distributed optimization
Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck, Francis Bach, and Laurent Massoulie · 2020
Closest in time.
On the optimal combination of tensor optimization methods
Dmitry Kamzolov, Alexander Gasnikov, and Pavel Dvurechensky · 2020
Closest in time.
Inexact accelerated high-order proximal-point methods
Yurii Nesterov · 2020
Closest in time.
Inexact high-order proximal-point methods with auxiliary search procedure
Yurii Nesterov · 2020
Closest in time.
Superfast second-order methods for unconstrained convex optimization
Yurii Nesterov · 2020
Closest in time.