Cover Image

Decentralized Inexact Cubic Newton Method with Consensus Procedure

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Agafonov, Artem, Novitskii, Anton, Rogozin, Alexander, Sokolov, Yury, Kamzolov, Dmitry, Dyakonov, Alexander, Takáč, Martin, Gasnikov, Alexander
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

OPTAMI: Global Superlinear Convergence of High-order Methods
by: Kamzolov, Dmitry, et al.
Published: (2024)

Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
by: Agafonov, Artem, et al.
Published: (2024)

Exploiting higher-order derivatives in convex optimization methods
by: Kamzolov, Dmitry, et al.
Published: (2022)

Adaptive Regularized Newton Method with Inexact Hessian
by: Shestakov, Aleksandr, et al.
Published: (2025)

Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness
by: Agafonov, Artem, et al.
Published: (2023)

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
by: Agafonov, Artem, et al.
Published: (2025)

Cubic Regularized Newton Method with Variance Reduction for Finite-sum Non-convex Problems
by: Pasechnyuk-Vilensky, Dmitry, et al.
Published: (2025)

Decentralized Optimization with Coupled Constraints
by: Yarmoshik, Demyan, et al.
Published: (2024)

Decentralized Finite-Sum Optimization over Time-Varying Networks
by: Metelev, Dmitry, et al.
Published: (2024)

Decentralized Optimization with Mixed Affine Constraints
by: Yarmoshik, Demyan, et al.
Published: (2026)

Dual Smoothing for Decentralized Optimization
by: Rogozin, Alexander, et al.
Published: (2025)

AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
by: Ostroukhov, Petr, et al.
Published: (2024)

The Mirror-Prox Sliding Method for Non-smooth decentralized saddle-point problems
by: Kuruzov, Ilya, et al.
Published: (2022)

Method with Batching for Stochastic Finite-Sum Variational Inequalities in Non-Euclidean Setting
by: Pichugin, Alexander, et al.
Published: (2024)

Decentralized Distributed Optimization for Saddle Point Problems
by: Rogozin, Alexander, et al.
Published: (2021)

Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under $(L_0,L_1)$-Smoothness
by: Lobanov, Aleksandr, et al.
Published: (2024)

A Parameter-Free Zeroth-Order Algorithm for Decentralized Stochastic Convex Optimization
by: Chen, Jiawei, et al.
Published: (2026)

Average-case optimization analysis for distributed consensus algorithms on regular graphs
by: Nguyen, Nhat Trung, et al.
Published: (2024)

Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
by: Beznosikov, Aleksandr, et al.
Published: (2026)

Lower and upper bounds of the convergence rate of gradient methods with composite noise in gradient
by: Vasin, Artem, et al.
Published: (2026)

Loss-Transformation Invariance in the Damped Newton Method
by: Shestakov, Alexander, et al.
Published: (2025)

Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks
by: Divilkovskiy, Maxim, et al.
Published: (2025)

Higher Degree Inexact Model for Optimization problems
by: Alkousa, Mohammad, et al.
Published: (2024)

Newton Method Revisited: Global Convergence Rates up to $\mathcal {O}\left(k^{-3} \right)$ for Stepsize Schedules and Linesearch Procedures
by: Hanzely, Slavomír, et al.
Published: (2024)

Stochastic Gradient Methods with Preconditioned Updates
by: Sadiev, Abdurakhmon, et al.
Published: (2022)

SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms
by: Abdukhakimov, Farshed, et al.
Published: (2023)

Gradient-Type Methods For Decentralized Optimization Problems With Polyak-Łojasiewicz Condition Over Time-Varying Networks
by: Kuruzov, Ilya, et al.
Published: (2022)

Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
by: Kovalev, Dmitry, et al.
Published: (2024)

Accelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks
by: Solodkin, Vladimir, et al.
Published: (2024)

Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem under "Overparametrization" Condition
by: Lobanov, Aleksandr, et al.
Published: (2023)

Strengthening the finite characterizations of smooth min-max games
by: Krivchenko, Valery, et al.
Published: (2026)

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)

Adaptive Stepsize Selection in Decentralized Convex Optimization
by: Kuruzov, Ilya, et al.
Published: (2025)

Complexity Guarantees for Nonconvex Newton-MR Under Inexact Hessian Information
by: Lim, Alexander, et al.
Published: (2023)

A Parameter-free Decentralized Algorithm for Composite Convex Optimization
by: Chen, Xiaokai, et al.
Published: (2025)

Bregman Proximal Method for Efficient Communications under Similarity
by: Beznosikov, Aleksandr, et al.
Published: (2023)

Decentralized Personalized Federated Learning for Min-Max Problems
by: Borodich, Ekaterina, et al.
Published: (2021)

Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities
by: Pichugin, Alexander, et al.
Published: (2024)

$ψ$DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning
by: Ziu, Klea, et al.
Published: (2024)

The Black-Box Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation
by: Lobanov, Aleksandr, et al.
Published: (2023)