Cover Image

On Convergence of Incremental Gradient for Non-Convex Smooth Functions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Koloskova, Anastasia, Doikov, Nikita, Stich, Sebastian U., Jaggi, Martin
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
by: Doikov, Nikita, et al.
Published: (2024)

Gradient-Normalized Smoothness for Optimization with Approximate Hessians
by: Semenov, Andrei, et al.
Published: (2025)

Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
by: Chayti, El Mahdi, et al.
Published: (2023)

FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
by: Takezawa, Yuki, et al.
Published: (2025)

Improving Stochastic Cubic Newton with Momentum
by: Chayti, El Mahdi, et al.
Published: (2024)

Non-Convex Federated Optimization under Cost-Aware Client Selection
by: Jiang, Xiaowen, et al.
Published: (2025)

Non-convex Stochastic Composite Optimization with Polyak Momentum
by: Gao, Yuan, et al.
Published: (2024)

Complexity of Minimizing Regularized Convex Quadratic Functions
by: Thomsen, Daniel Berg, et al.
Published: (2024)

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
by: Kovalev, Dmitry
Published: (2026)

Stochastic Difference-of-Convex Optimization with Momentum
by: Chayti, El Mahdi, et al.
Published: (2025)

Scalable Decentralized Learning with Teleportation
by: Takezawa, Yuki, et al.
Published: (2025)

Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
by: Zhang, Siqi, et al.
Published: (2023)

The Privacy Power of Correlated Noise in Decentralized Learning
by: Allouah, Youssef, et al.
Published: (2024)

Avoiding Bias in Clipped SGD for Overparameterized Models under Generalized Smoothness
by: Lobanov, Aleksandr, et al.
Published: (2026)

A New First-Order Meta-Learning Algorithm with Convergence Guarantees
by: Chayti, El Mahdi, et al.
Published: (2024)

Cubic regularized subspace Newton for non-convex optimization
by: Zhao, Jim, et al.
Published: (2024)

Locally Adaptive Federated Learning
by: Mukherjee, Sohom, et al.
Published: (2023)

Federated Optimization with Doubly Regularized Drift Correction
by: Jiang, Xiaowen, et al.
Published: (2024)

Stabilized Proximal-Point Methods for Federated Optimization
by: Jiang, Xiaowen, et al.
Published: (2024)

Directional Smoothness and Gradient Methods: Convergence and Adaptivity
by: Mishkin, Aaron, et al.
Published: (2024)

An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
by: Cao, Jincheng, et al.
Published: (2024)

Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods
by: Vankov, Daniil, et al.
Published: (2024)

Accelerated Distributed Optimization with Compression and Error Feedback
by: Gao, Yuan, et al.
Published: (2025)

Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
by: Takezawa, Yuki, et al.
Published: (2025)

Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential
by: Zheng, Yuping, et al.
Published: (2025)

Muon Does Not Converge on Convex Lipschitz Functions
by: Parshakova, Tetiana, et al.
Published: (2026)

Gradient Regularized Newton Boosting Trees with Global Convergence
by: Zozoulenko, Nikita, et al.
Published: (2026)

Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
by: Yu, Yaxin, et al.
Published: (2026)

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
by: Kornilov, Nikita, et al.
Published: (2025)

Towards Faster Decentralized Stochastic Optimization with Communication Compression
by: Islamov, Rustem, et al.
Published: (2024)

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
by: Demidovich, Yury, et al.
Published: (2024)

Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
by: Palenzuela, Karlo, et al.
Published: (2025)

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)

Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
by: Sato, Kanji, et al.
Published: (2022)

Convergence of Steepest Descent and Adam under Non-Uniform Smoothness
by: Vaswani, Sharan, et al.
Published: (2026)

Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
by: Chezhegov, Savelii, et al.
Published: (2025)

A Randomized Linearly Convergent Frank-Wolfe-type Method for Smooth Convex Minimization over the Spectrahedron
by: Garber, Dan
Published: (2025)

On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms
by: Kovalev, Dmitry, et al.
Published: (2024)

Smooth Quasar-Convex Optimization with Constraints
by: Martínez-Rubio, David
Published: (2025)

A Split-Client Approach to Second-Order Optimization
by: Chayti, El Mahdi, et al.
Published: (2025)