Imagen de Portada

Shuffling the Stochastic Mirror Descent via Dual Lipschitz Continuity and Kernel Conditioning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Qiu, Junwen, Mei, Leilei, Zhang, Junyu
Formato:	Preprint
Publicado:	2026
Materias:	Optimization and Control Machine Learning 90C26, 90C06, 90C15
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A New Kernel Regularity Condition for Distributed Mirror Descent: Broader Coverage and Simpler Analysis
por: Qiu, Junwen, et al.
Publicado: (2026)

A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods
por: Qiu, Junwen, et al.
Publicado: (2024)

Stochastic First-Order Methods with Non-smooth and Non-Euclidean Proximal Terms for Nonconvex High-Dimensional Stochastic Optimization
por: Xie, Yue, et al.
Publicado: (2024)

A Normal Map-Based Proximal Stochastic Gradient Method: Convergence and Identification Properties
por: Qiu, Junwen, et al.
Publicado: (2023)

Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
por: Fatkhullin, Ilyas, et al.
Publicado: (2024)

On Tackling High-Dimensional Nonconvex Stochastic Optimization via Stochastic First-Order Methods with Non-smooth Proximal Terms and Variance Reduction
por: Xie, Yue, et al.
Publicado: (2025)

A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization
por: Qiu, Junwen, et al.
Publicado: (2023)

Stochastic Gradient Descent Revisited
por: Louzi, Azar
Publicado: (2024)

High Probability Guarantees for Random Reshuffling
por: Yu, Hengxu, et al.
Publicado: (2023)

A Generalized Version of Chung's Lemma and its Applications
por: Jiang, Li, et al.
Publicado: (2024)

Stochastic Optimization under Hidden Convexity
por: Fatkhullin, Ilyas, et al.
Publicado: (2023)

Random Reshuffling with Momentum for Nonconvex Problems: Iteration Complexity and Last Iterate Convergence
por: Qiu, Junwen, et al.
Publicado: (2024)

Bilevel Learning via Inexact Stochastic Gradient Descent
por: Salehi, Mohammad Sadegh, et al.
Publicado: (2025)

Alternating minimization for square root principal component pursuit
por: Deng, Shengxiang, et al.
Publicado: (2024)

Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
por: Fatkhullin, Ilyas, et al.
Publicado: (2025)

New vector transport operators extending a Riemannian CG algorithm to generalized Stiefel manifold with low-rank applications
por: Wang, Xuejie, et al.
Publicado: (2023)

An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization
por: Engquist, Björn, et al.
Publicado: (2022)

Fully First-Order Methods for Decentralized Bilevel Optimization
por: Wang, Xiaoyu, et al.
Publicado: (2024)

Stochastic block coordinate and function alternation for multi-objective optimization and learning
por: Tran, Trang H., et al.
Publicado: (2026)

Optimization without Retraction on the Random Generalized Stiefel Manifold
por: Vary, Simon, et al.
Publicado: (2024)

Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
por: Lapucci, Matteo, et al.
Publicado: (2024)

Optimal Asymptotic Rates for (Stochastic) Gradient Descent under the Local PL-Condition: A Geometric Approach
por: Kassing, Sebastian, et al.
Publicado: (2026)

Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization
por: Higuchi, Rei, et al.
Publicado: (2024)

A new envelope function for nonsmooth DC optimization
por: Themelis, Andreas, et al.
Publicado: (2020)

On convergence of a $q$-random coordinate constrained algorithm for non-convex problems
por: Ghaffari-Hadigheh, Alireza, et al.
Publicado: (2022)

A Stochastic Quasi-Newton Method in the Absence of Common Random Numbers
por: Menickelly, Matt, et al.
Publicado: (2023)

Randomized block proximal method with locally Lipschitz continuous gradient
por: Pérez-Aros, Pedro, et al.
Publicado: (2025)

A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction
por: Milzarek, Andre, et al.
Publicado: (2022)

Boosted Stochastic Frank-Wolfe for Constrained Nonconvex Optimization
por: Nandhan, Navil, et al.
Publicado: (2026)

Stability and convergence analysis of AdaGrad for non-convex optimization via novel stopping time-based techniques
por: Jin, Ruinan, et al.
Publicado: (2024)

Decentralized projected Riemannian stochastic recursive momentum method for nonconvex optimization
por: Deng, Kangkang, et al.
Publicado: (2024)

A Unifying Convexification Framework for Chance-Constrained Programs via Bilinear Extended Formulations over a Simplex
por: Davarnia, Danial, et al.
Publicado: (2025)

MoSSP: A Momentum-Based Single-Loop Stochastic Penalty Method for Nonconvex Constrained DC-Regularized Optimization
por: Li, Luxuan, et al.
Publicado: (2026)

Stochastic Algorithms for Large-Scale Composite Optimization: the Case of Single-Shot X-FEL Imaging
por: Luke, D. Russell, et al.
Publicado: (2024)

Tractable downfall of basis pursuit in structured sparse optimization
por: Marmary, Maya V., et al.
Publicado: (2025)

Lagrangian cuts generated by batch to efficiently solve two-stage stochastic mixed-integer program
por: Xiaoyu, Luo, et al.
Publicado: (2024)

Extended Triangle Inequalities for Nonconvex Box-Constrained Quadratic Programming
por: Anstreicher, Kurt M., et al.
Publicado: (2025)

Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
por: Lapucci, Matteo, et al.
Publicado: (2024)

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions
por: Bao, Chenglong, et al.
Publicado: (2025)

Pareto-optimal Trade-offs Between Communication and Computation with Flexible Gradient Tracking
por: Huang, Yan, et al.
Publicado: (2025)