Image de couverture de livre

The Stochastic Multi-Proximal Method for Nonsmooth Optimization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Condat, Laurent, Gasanov, Elnur, Richtárik, Peter
Format:	Preprint
Publié:	2025
Sujets:	Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Speeding up Stochastic Proximal Optimization in the High Hessian Dissimilarity Setting
par: Gasanov, Elnur, et autres
Publié: (2024)

Stochastic Proximal Point Methods for Monotone Inclusions under Expected Similarity
par: Sadiev, Abdurakhmon, et autres
Publié: (2024)

A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization
par: Condat, Laurent, et autres
Publié: (2026)

Revisiting Stochastic Proximal Point Methods: Generalized Smoothness and Similarity
par: Tovmasyan, Zhirayr, et autres
Publié: (2025)

Convergence Analysis of the PAGE Stochastic Algorithm for Weakly Convex Finite-Sum Optimization
par: Condat, Laurent, et autres
Publié: (2025)

A Simple Linear Convergence Analysis of the Point-SAGA Algorithm
par: Condat, Laurent, et autres
Publié: (2024)

Tight Lower Bounds and Optimal Algorithms for Stochastic Nonconvex Optimization with Heavy-Tailed Noise
par: Fradin, Adrien, et autres
Publié: (2025)

A Unified Primal-Dual Recipe for Accelerating Three-Operator Splitting Methods
par: Sadiev, Abdurakhmon, et autres
Publié: (2026)

BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training
par: Condat, Laurent, et autres
Publié: (2026)

Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants
par: Richtárik, Peter, et autres
Publié: (2024)

TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation
par: Condat, Laurent, et autres
Publié: (2023)

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
par: Condat, Laurent, et autres
Publié: (2024)

Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning
par: Meinhardt, Georg, et autres
Publié: (2024)

A Unified Theory of Stochastic Proximal Point Methods without Smoothness
par: Richtárik, Peter, et autres
Publié: (2024)

On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization
par: Tyurin, Alexander, et autres
Publié: (2024)

A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems
par: Wang, Qingsong, et autres
Publié: (2024)

Smoothing Accelerated Proximal Gradient Method with Backtracking for Nonsmooth Multiobjective Optimization
par: Chengzhi, Huang
Publié: (2025)

An Adaptive Proximal Point Method for Nonsmooth and Nonconvex Optimization on Hadamard Manifolds
par: Amaral, Vitaliano S., et autres
Publié: (2025)

Smoothing Accelerated Proximal Gradient Method with Fast Convergence Rate for Nonsmooth Multi-objective Optimization
par: Huang, Chengzhi
Publié: (2023)

CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Training and Compression in Distributed Optimization
par: Condat, Laurent, et autres
Publié: (2022)

Stochastic Bregman Subgradient Methods for Nonsmooth Nonconvex Optimization Problems
par: Ding, Kuangyu, et autres
Publié: (2024)

Stabilized Proximal Point Method via Trust Region Control
par: Li, Hanmin, et autres
Publié: (2026)

A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
par: Diouane, Youssef, et autres
Publié: (2024)

Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
par: Guo, Luyao, et autres
Publié: (2023)

An Inexact Proximal Framework for Nonsmooth Riemannian Difference-of-Convex Optimization
par: Jiang, Bo, et autres
Publié: (2025)

Continuation Method for Nonsmooth Model Predictive Control Using Proximal Technique
par: Shima, Ryotaro, et autres
Publié: (2024)

Zeroth-order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization
par: Marrinan, Luke, et autres
Publié: (2023)

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning
par: Karagulyan, Avetik, et autres
Publié: (2024)

Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization
par: Chen, Ziyi, et autres
Publié: (2025)

The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications
par: Gruntkowska, Kaja, et autres
Publié: (2025)

Non-Euclidean Broximal Point Method: A Blueprint for Geometry-Aware Optimization
par: Gruntkowska, Kaja, et autres
Publié: (2025)

Proximal Dogleg Opportunistic Majorization for Nonconvex and Nonsmooth Optimization
par: Zhou, Yiming, et autres
Publié: (2024)

Equilibrium Invariance, Proximality, and Surrogation: Moreau-Smoothed Best-Response Pathways in Stochastic Nonsmooth Games
par: Xiao, Zhuoyu, et autres
Publié: (2026)

Subgame Perfect Methods in Nonsmooth Convex Optimization
par: Grimmer, Benjamin, et autres
Publié: (2025)

Subgradient Gliding Method for Nonsmooth Convex Optimization
par: Zhu, Zhihan, et autres
Publié: (2026)

A Single-loop Stochastic Riemannian ADMM for Nonsmooth Optimization
par: Jin, Jiachen, et autres
Publié: (2025)

A Zeroth-order Proximal Stochastic Gradient Method for Weakly Convex Stochastic Optimization
par: Pougkakiotis, Spyridon, et autres
Publié: (2022)

Hinge-Proximal Stochastic Gradient Methods for Convex Optimization with Functional Constraints
par: Rajoriya, Vaibhav, et autres
Publié: (2025)

Inexact Zeroth-Order Nonsmooth and Nonconvex Stochastic Composite Optimization and Applications
par: Pougkakiotis, Spyridon, et autres
Publié: (2025)

Faster Stochastic ADMM for Nonsmooth Composite Convex Optimization in Hilbert Space
par: Deng, Weihua, et autres
Publié: (2026)