Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo Line Search
Fuente:
arXiv
Saved in:
| Main Authors: | Tsukada, Yuki, Iiduka, Hideaki |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
by: Oowada, Kanata, et al.
Published: (2025)
by: Oowada, Kanata, et al.
Published: (2025)
Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent
by: Umeda, Hikaru, et al.
Published: (2024)
by: Umeda, Hikaru, 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)
Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity
by: Umeda, Hikaru, et al.
Published: (2025)
by: Umeda, Hikaru, et al.
Published: (2025)
Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
by: Sato, Naoki, et al.
Published: (2024)
by: Sato, Naoki, et al.
Published: (2024)
Improved Convergence Rates of Muon Optimizer for Nonconvex Optimization
by: Nagashima, Shuntaro, et al.
Published: (2026)
by: Nagashima, Shuntaro, et al.
Published: (2026)
Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate
by: Harada, Hinata, et al.
Published: (2024)
by: Harada, Hinata, et al.
Published: (2024)
Both Asymptotic and Non-Asymptotic Convergence of Quasi-Hyperbolic Momentum using Increasing Batch Size
by: Imaizumi, Kento, et al.
Published: (2025)
by: Imaizumi, Kento, et al.
Published: (2025)
Muon Converges under Heavy-Tailed Noise: Nonconvex Hölder-Smooth Empirical Risk Minimization
by: Iiduka, Hideaki
Published: (2026)
by: Iiduka, Hideaki
Published: (2026)
Optimal Growth Schedules for Batch Size and Learning Rate in SGD that Reduce SFO Complexity
by: Umeda, Hikaru, et al.
Published: (2025)
by: Umeda, Hikaru, et al.
Published: (2025)
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
by: Vaswani, Sharan, et al.
Published: (2025)
by: Vaswani, Sharan, et al.
Published: (2025)
Mini-Batch Stochastic Halpern Algorithm for Nonexpansive Fixed point Problems
by: Iiduka, Hideaki
Published: (2026)
by: Iiduka, Hideaki
Published: (2026)
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
by: Köhne, Frederik, et al.
Published: (2023)
by: Köhne, Frederik, et al.
Published: (2023)
Increasing Batch Size Improves Convergence of Stochastic Gradient Descent with Momentum
by: Kamo, Keisuke, et al.
Published: (2025)
by: Kamo, Keisuke, et al.
Published: (2025)
On the Role of Batch Size in Stochastic Conditional Gradient Methods
by: Islamov, Rustem, et al.
Published: (2026)
by: Islamov, Rustem, et al.
Published: (2026)
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
by: Lin, Tianyi, et al.
Published: (2024)
by: Lin, Tianyi, et al.
Published: (2024)
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
by: Lin, Tianyi, et al.
Published: (2019)
by: Lin, Tianyi, et al.
Published: (2019)
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
by: Chen, Lesi, et al.
Published: (2023)
by: Chen, Lesi, et al.
Published: (2023)
Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
by: Liu, Wei, et al.
Published: (2025)
by: Liu, Wei, et al.
Published: (2025)
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
by: Ostroukhov, Petr, et al.
Published: (2024)
by: Ostroukhov, Petr, et al.
Published: (2024)
Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
by: Meng, Si Yi, et al.
Published: (2024)
by: Meng, Si Yi, et al.
Published: (2024)
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
by: Zhang, Gavin, et al.
Published: (2025)
by: Zhang, Gavin, et al.
Published: (2025)
Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search
by: Cheng, Nuojin, et al.
Published: (2025)
by: Cheng, Nuojin, et al.
Published: (2025)
Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need
by: Zheng, Xinran, et al.
Published: (2026)
by: Zheng, Xinran, et al.
Published: (2026)
The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)
by: Livni, Roi
Published: (2024)
Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)
by: Aujol, Jean-François, et al.
Published: (2025)
Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere?
by: Meng, Si Yi, et al.
Published: (2025)
by: Meng, Si Yi, et al.
Published: (2025)
Mini-Batch Stochastic Krasnosel'ski\uı-Mann Algorithm for Nonexpansive Fixed Point Problems
by: Iiduka, Hideaki
Published: (2026)
by: Iiduka, Hideaki
Published: (2026)
Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization
by: Vansover-Hager, Shira, et al.
Published: (2025)
by: Vansover-Hager, Shira, et al.
Published: (2025)
Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)
by: Che, Ethan, et al.
Published: (2024)
Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)
by: Jiang, Nanfei, et al.
Published: (2025)
Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients
by: Oikonomou, Dimitris, et al.
Published: (2025)
by: Oikonomou, Dimitris, et al.
Published: (2025)
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
by: Zhang, Gavin, et al.
Published: (2022)
by: Zhang, Gavin, et al.
Published: (2022)
Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
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)
Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
by: Riedl, Konstantin, et al.
Published: (2023)
by: Riedl, Konstantin, et al.
Published: (2023)
Derivatives of Stochastic Gradient Descent in parametric optimization
by: Iutzeler, Franck, et al.
Published: (2024)
by: Iutzeler, Franck, 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)
Projective Proximal Gradient Descent for A Class of Nonconvex Nonsmooth Optimization Problems: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property
by: Yang, Yingzhen, et al.
Published: (2023)
by: Yang, Yingzhen, et al.
Published: (2023)
More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)
by: Partohaghighi, Mohammad, et al.
Published: (2025)
Similar Items
-
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
by: Oowada, Kanata, et al.
Published: (2025) -
Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent
by: Umeda, Hikaru, et al.
Published: (2024) -
Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization
by: Sato, Naoki, et al.
Published: (2023) -
Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity
by: Umeda, Hikaru, et al.
Published: (2025) -
Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
by: Sato, Naoki, et al.
Published: (2024)