Convergence of the Iterates of the Stochastic Proximal Gradient Method
Fuente:
arXiv
Saved in:
| Main Author: | Madariaga, Javier I. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
An Abstract Stochastic Haugazeau Method for Best Approximation
by: Madariaga, Javier I.
Published: (2026)
by: Madariaga, Javier I.
Published: (2026)
A Geometric Framework for Stochastic Iterations
by: Combettes, Patrick L., et al.
Published: (2025)
by: Combettes, Patrick L., et al.
Published: (2025)
Almost-Surely Convergent Randomly Activated Monotone Operator Splitting Methods
by: Combettes, Patrick L., et al.
Published: (2024)
by: Combettes, Patrick L., et al.
Published: (2024)
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023)
by: Liu, Zijian, et al.
Published: (2023)
Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)
Convergence Rate Analysis for Monotone Accelerated Proximal Gradient Method
by: Wang, Zepeng, et al.
Published: (2025)
by: Wang, Zepeng, et al.
Published: (2025)
Asymptotic Analysis of an Abstract Stochastic Scheme for Solving Monotone Inclusions
by: Combettes, Patrick L., et al.
Published: (2025)
by: Combettes, Patrick L., et al.
Published: (2025)
A Zeroth-order Proximal Stochastic Gradient Method for Weakly Convex Stochastic Optimization
by: Pougkakiotis, Spyridon, et al.
Published: (2022)
by: Pougkakiotis, Spyridon, et al.
Published: (2022)
On the Last-Iterate Convergence of Shuffling Gradient Methods
by: Liu, Zijian, et al.
Published: (2024)
by: Liu, Zijian, et al.
Published: (2024)
Hinge-Proximal Stochastic Gradient Methods for Convex Optimization with Functional Constraints
by: Rajoriya, Vaibhav, et al.
Published: (2025)
by: Rajoriya, Vaibhav, et al.
Published: (2025)
Boosting Accelerated Proximal Gradient Method with Adaptive Sampling for Stochastic Composite Optimization
by: Zhu, Dongxuan, et al.
Published: (2025)
by: Zhu, Dongxuan, et al.
Published: (2025)
Distributed Normal Map-based Stochastic Proximal Gradient Methods over Networks
by: Huang, Kun, et al.
Published: (2024)
by: Huang, Kun, et al.
Published: (2024)
A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems
by: Wang, Qingsong, et al.
Published: (2024)
by: Wang, Qingsong, et al.
Published: (2024)
Last Iterate Convergence of Popov Method for Non-monotone Stochastic Variational Inequalities
by: Vankov, Daniil, et al.
Published: (2023)
by: Vankov, Daniil, et al.
Published: (2023)
Smoothing Accelerated Proximal Gradient Method with Fast Convergence Rate for Nonsmooth Multi-objective Optimization
by: Huang, Chengzhi
Published: (2023)
by: Huang, Chengzhi
Published: (2023)
Stochastic Bregman Proximal Gradient Method Revisited: Kernel Conditioning and Painless Variance Reduction
by: Zhang, Junyu
Published: (2024)
by: Zhang, Junyu
Published: (2024)
Last-Iterate Convergence of Anchored Gradient Descent
by: Cai, Yang, et al.
Published: (2026)
by: Cai, Yang, et al.
Published: (2026)
Convergence Analysis of Stochastic Accelerated Gradient Methods for Generalized Smooth Optimizations
by: Yu, Chenhao, et al.
Published: (2025)
by: Yu, Chenhao, et al.
Published: (2025)
La Méthode du Gradient Proximé
by: Combettes, Patrick L.
Published: (2025)
by: Combettes, Patrick L.
Published: (2025)
On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer
by: Chakrabarti, Kushal, et al.
Published: (2024)
by: Chakrabarti, Kushal, et al.
Published: (2024)
Linear Convergence of the Proximal Gradient Method for Composite Optimization Under the Polyak-Łojasiewicz Inequality and Its Variant
by: Kong, Qingyuan, et al.
Published: (2024)
by: Kong, Qingyuan, et al.
Published: (2024)
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
by: Ding, Kuangyu, et al.
Published: (2023)
by: Ding, Kuangyu, et al.
Published: (2023)
On the Convergence and Complexity of the Stochastic Central Finite-Difference Based Gradient Estimation Methods
by: Bollapragada, Raghu, et al.
Published: (2025)
by: Bollapragada, Raghu, et al.
Published: (2025)
On the Convergence of the Accelerated Riccati Iteration Method
by: Rajasingam, Prasanthan, et al.
Published: (2017)
by: Rajasingam, Prasanthan, et al.
Published: (2017)
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
by: Ding, Dongsheng, et al.
Published: (2023)
by: Ding, Dongsheng, et al.
Published: (2023)
Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications
by: Gokhale, Anand, et al.
Published: (2024)
by: Gokhale, Anand, et al.
Published: (2024)
Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
by: Martínez-Rubio, David, et al.
Published: (2024)
by: Martínez-Rubio, David, et al.
Published: (2024)
Convergence of Nonmonotone Proximal Gradient Methods under the Kurdyka-Lojasiewicz Property without a Global Lipschitz Assumption
by: Kanzow, Christian, et al.
Published: (2024)
by: Kanzow, Christian, et al.
Published: (2024)
The Stochastic Multi-Proximal Method for Nonsmooth Optimization
by: Condat, Laurent, et al.
Published: (2025)
by: Condat, Laurent, et al.
Published: (2025)
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
by: Liang, Ling, et al.
Published: (2024)
by: Liang, Ling, et al.
Published: (2024)
Universal Adaptive Proximal Gradient Methods via Gradient Mapping Accumulation
by: Wang, Zimeng, et al.
Published: (2026)
by: Wang, Zimeng, et al.
Published: (2026)
Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)
by: Liu, Zijian, et al.
Published: (2025)
Accelerated Proximal Gradient Method with Backtracking for Multiobjective Optimization
by: Huang, Chengzhi, et al.
Published: (2024)
by: Huang, Chengzhi, et al.
Published: (2024)
Improved Convergence for Decentralized Stochastic Optimization with Biased Gradients
by: Xu, Qing, et al.
Published: (2026)
by: Xu, Qing, et al.
Published: (2026)
Convergence Rates for Stochastic Proximal and Projection Estimators
by: Morales, Diego, et al.
Published: (2026)
by: Morales, Diego, et al.
Published: (2026)
A Normal Map-Based Proximal Stochastic Gradient Method: Convergence and Identification Properties
by: Qiu, Junwen, et al.
Published: (2023)
by: Qiu, Junwen, et al.
Published: (2023)
In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
by: Liu, Zijian
Published: (2026)
by: Liu, Zijian
Published: (2026)
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
by: Klein, Sara, et al.
Published: (2023)
by: Klein, Sara, et al.
Published: (2023)
Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
by: Mukherjee, Amartya, et al.
Published: (2025)
by: Mukherjee, Amartya, et al.
Published: (2025)
Revisiting Superlinear Convergence of Proximal Newton-Like Methods to Degenerate Solutions
by: Lee, Ching-pei, et al.
Published: (2026)
by: Lee, Ching-pei, et al.
Published: (2026)
Similar Items
-
An Abstract Stochastic Haugazeau Method for Best Approximation
by: Madariaga, Javier I.
Published: (2026) -
A Geometric Framework for Stochastic Iterations
by: Combettes, Patrick L., et al.
Published: (2025) -
Almost-Surely Convergent Randomly Activated Monotone Operator Splitting Methods
by: Combettes, Patrick L., et al.
Published: (2024) -
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023) -
Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)