Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Xiao, Zhao, Lei, Zhu, Daoli, So, Anthony Man-Cho |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Unified Analysis on the Subgradient Upper Bounds for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions
by: Zhu, Daoli, et al.
Published: (2023)
by: Zhu, Daoli, et al.
Published: (2023)
Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
by: He, Qi, et al.
Published: (2025)
by: He, Qi, et al.
Published: (2025)
Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis
by: Li, Jiajin, et al.
Published: (2022)
by: Li, Jiajin, et al.
Published: (2022)
Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
by: Zhang, Siyuan, et al.
Published: (2024)
by: Zhang, Siyuan, et al.
Published: (2024)
$\ell_1$-norm rank-one symmetric matrix factorization has no spurious second-order stationary points
by: Guan, Jiewen, et al.
Published: (2024)
by: Guan, Jiewen, et al.
Published: (2024)
On subdifferential chain rule of matrix factorization and beyond
by: Guan, Jiewen, et al.
Published: (2024)
by: Guan, Jiewen, et al.
Published: (2024)
Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithms
by: Chen, He, et al.
Published: (2024)
by: Chen, He, et al.
Published: (2024)
Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)
by: Zhu, Linglingzhi, et al.
Published: (2026)
On the Complexity of Finding Small Subgradients in Nonsmooth Optimization
by: Kornowski, Guy, et al.
Published: (2022)
by: Kornowski, Guy, et al.
Published: (2022)
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization
by: Grimmer, Benjamin, et al.
Published: (2023)
by: Grimmer, Benjamin, et al.
Published: (2023)
Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity
by: Gao, Wenzhi, et al.
Published: (2024)
by: Gao, Wenzhi, et al.
Published: (2024)
Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization
by: Xiao, Nachuan, et al.
Published: (2023)
by: Xiao, Nachuan, et al.
Published: (2023)
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023)
by: Liu, Zijian, et al.
Published: (2023)
Muon Does Not Converge on Convex Lipschitz Functions
by: Parshakova, Tetiana, et al.
Published: (2026)
by: Parshakova, Tetiana, et al.
Published: (2026)
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification
by: Jung, Hyunji, et al.
Published: (2025)
by: Jung, Hyunji, et al.
Published: (2025)
De-singularity Subgradient for the $q$-th-Powered $\ell_p$-Norm Weber Location Problem
by: Lai, Zhao-Rong, et al.
Published: (2024)
by: Lai, Zhao-Rong, et al.
Published: (2024)
Untangling Lariats: Subgradient Following of Variationally Penalized Objectives
by: Mo, Kai-Chia, et al.
Published: (2024)
by: Mo, Kai-Chia, et al.
Published: (2024)
Randomized Submanifold Subgradient Method for Optimization over Stiefel Manifolds
by: Cheung, Andy Yat-Ming, et al.
Published: (2024)
by: Cheung, Andy Yat-Ming, et al.
Published: (2024)
The Stochastic Conjugate Subgradient Algorithm For Kernel Support Vector Machines
by: Zhang, Di, et al.
Published: (2024)
by: Zhang, Di, et al.
Published: (2024)
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
by: Klein, Sara, et al.
Published: (2023)
by: Klein, Sara, et al.
Published: (2023)
Last Iterate Convergence of Incremental Methods and Applications in Continual Learning
by: Cai, Xufeng, et al.
Published: (2024)
by: Cai, Xufeng, et al.
Published: (2024)
Computing Competitive Equilibrium for Chores: Linear Convergence and Lightweight Iteration
by: Chen, He, et al.
Published: (2024)
by: Chen, He, et al.
Published: (2024)
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
by: Sherman, Uri, et al.
Published: (2025)
by: Sherman, Uri, et al.
Published: (2025)
Revisiting Convergence of AdaGrad with Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)
by: Hong, Yusu, et al.
Published: (2024)
Generalized Continuous-Time Models for Nesterov's Accelerated Gradient Methods
by: Park, Chanwoong, et al.
Published: (2024)
by: Park, Chanwoong, et al.
Published: (2024)
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
by: Xiao, Nachuan, et al.
Published: (2023)
by: Xiao, Nachuan, et al.
Published: (2023)
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
by: Giampouras, Paris, et al.
Published: (2024)
by: Giampouras, Paris, et al.
Published: (2024)
Subgradient Method for System Identification with Non-Smooth Objectives
by: Yalcin, Baturalp, et al.
Published: (2025)
by: Yalcin, Baturalp, et al.
Published: (2025)
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
by: Xiao, Minheng, et al.
Published: (2024)
by: Xiao, Minheng, et al.
Published: (2024)
On the Global Convergence of Risk-Averse Natural Policy Gradient Methods with Expected Conditional Risk Measures
by: Yu, Xian, et al.
Published: (2023)
by: Yu, Xian, et al.
Published: (2023)
On the Oracle Complexity of a Riemannian Inexact Augmented Lagrangian Method for Riemannian Nonsmooth Composite Problems
by: Xu, Meng, et al.
Published: (2024)
by: Xu, Meng, et al.
Published: (2024)
Extragradient Method for $(L_0, L_1)$-Lipschitz Root-finding Problems
by: Choudhury, Sayantan, et al.
Published: (2025)
by: Choudhury, Sayantan, et al.
Published: (2025)
Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
by: Li, Yuchen, et al.
Published: (2024)
by: Li, Yuchen, et al.
Published: (2024)
Perturbed Iterate SGD for Lipschitz Continuous Loss Functions with Numerical Error and Adaptive Step Sizes
by: Metel, Michael R.
Published: (2022)
by: Metel, Michael R.
Published: (2022)
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
by: Sherman, Uri, et al.
Published: (2025)
by: Sherman, Uri, et al.
Published: (2025)
On the Last-Iterate Convergence of Shuffling Gradient Methods
by: Liu, Zijian, et al.
Published: (2024)
by: Liu, Zijian, et al.
Published: (2024)
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
by: Zhou, Dongruo, et al.
Published: (2018)
by: Zhou, Dongruo, et al.
Published: (2018)
Directional Smoothness and Gradient Methods: Convergence and Adaptivity
by: Mishkin, Aaron, et al.
Published: (2024)
by: Mishkin, Aaron, et al.
Published: (2024)
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
by: Yu, Qian, et al.
Published: (2024)
by: Yu, Qian, et al.
Published: (2024)
A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
by: Zhou, Yuhao, et al.
Published: (2025)
by: Zhou, Yuhao, et al.
Published: (2025)
Similar Items
-
A Unified Analysis on the Subgradient Upper Bounds for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions
by: Zhu, Daoli, et al.
Published: (2023) -
Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
by: He, Qi, et al.
Published: (2025) -
Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis
by: Li, Jiajin, et al.
Published: (2022) -
Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
by: Zhang, Siyuan, et al.
Published: (2024) -
$\ell_1$-norm rank-one symmetric matrix factorization has no spurious second-order stationary points
by: Guan, Jiewen, et al.
Published: (2024)