Saved in:
| Main Authors: | Yu, Chenhao, Hong, Yusu, Lin, Junhong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.11125 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)
by: Hong, Yusu, et al.
Published: (2024)
Revisiting Convergence of AdaGrad with Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)
by: Hong, Yusu, et al.
Published: (2024)
Generalized Stochastic Gradient Descent with Momentum Methods for Smooth Optimization
by: Wang, Zimeng, et al.
Published: (2026)
by: Wang, Zimeng, et al.
Published: (2026)
Smoothing Accelerated Proximal Gradient Method with Fast Convergence Rate for Nonsmooth Multi-objective Optimization
by: Huang, Chengzhi
Published: (2023)
by: Huang, Chengzhi
Published: (2023)
Adaptive Accelerated Gradient Method for Smooth Convex Optimization
by: Wang, Zepeng, et al.
Published: (2025)
by: Wang, Zepeng, et al.
Published: (2025)
Deterministic and Stochastic Accelerated Gradient Method for Convex Semi-Infinite Optimization
by: Yao, Yao, et al.
Published: (2023)
by: Yao, Yao, et al.
Published: (2023)
Point Convergence Analysis of the Accelerated Gradient Method for Multiobjective Optimization: Continuous and Discrete
by: Yin, Yingdong
Published: (2025)
by: Yin, Yingdong
Published: (2025)
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
by: Kim, Jungbin, et al.
Published: (2022)
by: Kim, Jungbin, et al.
Published: (2022)
Smoothing Accelerated Proximal Gradient Method with Backtracking for Nonsmooth Multiobjective Optimization
by: Chengzhi, Huang
Published: (2025)
by: Chengzhi, Huang
Published: (2025)
Convergence Rate Analysis for Monotone Accelerated Proximal Gradient Method
by: Wang, Zepeng, et al.
Published: (2025)
by: Wang, Zepeng, et al.
Published: (2025)
Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
by: Yu, Yaxin, et al.
Published: (2026)
by: Yu, Yaxin, et al.
Published: (2026)
Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization
by: Chen, Lesi, et al.
Published: (2025)
by: Chen, Lesi, et al.
Published: (2025)
Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and $(L_0, L_1)$-Smoothness
by: Tyurin, Alexander
Published: (2025)
by: Tyurin, Alexander
Published: (2025)
Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization
by: Liu, Tuo, et al.
Published: (2025)
by: Liu, Tuo, et al.
Published: (2025)
An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
by: Cao, Jincheng, et al.
Published: (2024)
by: Cao, Jincheng, et al.
Published: (2024)
Directional Smoothness and Gradient Methods: Convergence and Adaptivity
by: Mishkin, Aaron, et al.
Published: (2024)
by: Mishkin, Aaron, et al.
Published: (2024)
Convergence of the Iterates of the Stochastic Proximal Gradient Method
by: Madariaga, Javier I.
Published: (2026)
by: Madariaga, Javier I.
Published: (2026)
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
by: Pan, Rui, et al.
Published: (2023)
by: Pan, Rui, et al.
Published: (2023)
Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
by: Lin, Zhenwei, et al.
Published: (2023)
by: Lin, Zhenwei, et al.
Published: (2023)
Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence Rates
by: Vankov, Daniil, et al.
Published: (2024)
by: Vankov, Daniil, et al.
Published: (2024)
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)
Improved Convergence for Decentralized Stochastic Optimization with Biased Gradients
by: Xu, Qing, et al.
Published: (2026)
by: Xu, Qing, et al.
Published: (2026)
Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
by: Zhao, Yuheng, et al.
Published: (2025)
by: Zhao, Yuheng, et al.
Published: (2025)
Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization
by: Yang, Yufeng, et al.
Published: (2024)
by: Yang, Yufeng, et al.
Published: (2024)
Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
by: Mukherjee, Amartya, et al.
Published: (2025)
by: Mukherjee, Amartya, et al.
Published: (2025)
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
by: Klein, Sara, et al.
Published: (2023)
by: Klein, Sara, et al.
Published: (2023)
Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
by: Jiang, Zhanhong, et al.
Published: (2025)
by: Jiang, Zhanhong, et al.
Published: (2025)
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
by: Kovalev, Dmitry
Published: (2026)
by: Kovalev, Dmitry
Published: (2026)
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation
by: Mishkin, Aaron, et al.
Published: (2024)
by: Mishkin, Aaron, 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)
Point Convergence of Nesterov's Accelerated Gradient Method: An AI-Assisted Proof
by: Jang, Uijeong, et al.
Published: (2025)
by: Jang, Uijeong, et al.
Published: (2025)
Inexactly Smooth Performance Estimation and New Optimized Gradient Methods
by: Zoll, Aaron, et al.
Published: (2026)
by: Zoll, Aaron, et al.
Published: (2026)
Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
by: Oikonomidis, Konstantinos, et al.
Published: (2025)
by: Oikonomidis, Konstantinos, et al.
Published: (2025)
Improved Performance of Stochastic Gradients with Gaussian Smoothing
by: Starnes, Andrew, et al.
Published: (2023)
by: Starnes, Andrew, et al.
Published: (2023)
Universal Gradient Methods for Stochastic Convex Optimization
by: Rodomanov, Anton, et al.
Published: (2024)
by: Rodomanov, Anton, et al.
Published: (2024)
Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization
by: Sato, Naoki, et al.
Published: (2023)
by: Sato, Naoki, et al.
Published: (2023)
Accelerated Distance-adaptive Methods for Hölder Smooth and Convex Optimization
by: Ren, Yijin, et al.
Published: (2025)
by: Ren, Yijin, et al.
Published: (2025)
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)
An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
by: Gong, Xiaochuan, et al.
Published: (2024)
by: Gong, Xiaochuan, et al.
Published: (2024)
Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods
by: Vankov, Daniil, et al.
Published: (2024)
by: Vankov, Daniil, et al.
Published: (2024)
Similar Items
-
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024) -
Revisiting Convergence of AdaGrad with Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024) -
Generalized Stochastic Gradient Descent with Momentum Methods for Smooth Optimization
by: Wang, Zimeng, et al.
Published: (2026) -
Smoothing Accelerated Proximal Gradient Method with Fast Convergence Rate for Nonsmooth Multi-objective Optimization
by: Huang, Chengzhi
Published: (2023) -
Adaptive Accelerated Gradient Method for Smooth Convex Optimization
by: Wang, Zepeng, et al.
Published: (2025)