:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Sobotka, Jan, Šimánek, Petr, Kordík, Pavel
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2510.18783
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)

Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)

An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
by: Zhang, Jiahao, et al.
Published: (2024)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

Corner Gradient Descent
by: Yarotsky, Dmitry
Published: (2025)

Learning Provably Improves the Convergence of Gradient Descent
by: Song, Qingyu, et al.
Published: (2025)

The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
by: Daskalakis, Constantinos, et al.
Published: (2018)

Convergence Analysis of Fractional Gradient Descent
by: Aggarwal, Ashwani
Published: (2023)

Anytime Acceleration of Gradient Descent
by: Zhang, Zihan, et al.
Published: (2024)

On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
by: Qin, Zhen, et al.
Published: (2025)

Gauss-Newton Natural Gradient Descent for Shape Learning
by: King, James, et al.
Published: (2026)

Adaptive Conditional Gradient Descent
by: Khademi, Abbas, et al.
Published: (2025)

$k$-SVD with Gradient Descent
by: Jedra, Yassir, et al.
Published: (2025)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
by: Lin, Tianyi, et al.
Published: (2024)

Super Gradient Descent: Global Optimization requires Global Gradient
by: Achour, Seifeddine
Published: (2024)

RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
by: Fang, Cheng, et al.
Published: (2025)

Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization
by: Vansover-Hager, Shira, et al.
Published: (2025)

Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)

Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)

Unraveling the Gradient Descent Dynamics of Transformers
by: Song, Bingqing, et al.
Published: (2024)

Quantum Optimization via Gradient-Based Hamiltonian Descent
by: Leng, Jiaqi, et al.
Published: (2025)

Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization
by: Sato, Naoki, et al.
Published: (2023)

Mirror and Preconditioned Gradient Descent in Wasserstein Space
by: Bonet, Clément, et al.
Published: (2024)

Derivatives of Stochastic Gradient Descent in parametric optimization
by: Iutzeler, Franck, et al.
Published: (2024)

Convergence of Alternating Gradient Descent for Matrix Factorization
by: Ward, Rachel, et al.
Published: (2023)

On Penalty-based Bilevel Gradient Descent Method
by: Shen, Han, et al.
Published: (2023)

A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
by: Xu, Ziqing, et al.
Published: (2025)

Gradient Descent, Stochastic Optimization, and Other Tales
by: Lu, Jun
Published: (2022)

Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent
by: Umeda, Hikaru, et al.
Published: (2024)

A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization
by: Zhu, Yuchen, et al.
Published: (2024)

AutoGD: Automatic Learning Rate Selection for Gradient Descent
by: Surjanovic, Nikola, et al.
Published: (2025)

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law
by: Kunstner, Frederik, et al.
Published: (2025)

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
by: Shen, Wei, et al.
Published: (2023)

On the Inherent Privacy of Zeroth Order Projected Gradient Descent
by: Gupta, Devansh, et al.
Published: (2025)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
by: Lin, Tianyi, et al.
Published: (2019)

Gradient Descent's Last Iterate is Often (slightly) Suboptimal
by: Kornowski, Guy, et al.
Published: (2026)

Non-Euclidean Gradient Descent Operates at the Edge of Stability
by: Islamov, Rustem, et al.
Published: (2026)

Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
by: Köhne, Frederik, et al.
Published: (2023)