Cover Image

Adaptive Conditional Gradient Descent

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Khademi, Abbas, Silveti-Falls, Antonio
Format:	Preprint
Published:	2025
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Boosted Stochastic Frank-Wolfe for Constrained Nonconvex Optimization
by: Nandhan, Navil, et al.
Published: (2026)

On the Role of Batch Size in Stochastic Conditional Gradient Methods
by: Islamov, Rustem, et al.
Published: (2026)

Training Deep Learning Models with Norm-Constrained LMOs
by: Pethick, Thomas, et al.
Published: (2025)

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
by: Oikonomidis, Konstantinos, et al.
Published: (2026)

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

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

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

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

GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms
by: Eleh, Chinedu, et al.
Published: (2024)

Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
by: Kim, Yujun, et al.
Published: (2025)

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

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

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
by: Chakrabarti, Kushal, et al.
Published: (2021)

Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
by: Chakrabarti, Kushal, et al.
Published: (2020)

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

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

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

Enhancing Fractional Gradient Descent with Learned Optimizers
by: Sobotka, Jan, et al.
Published: (2025)

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)

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models
by: Behric, Lejs Deen, et al.
Published: (2025)

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

Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
by: Meng, Si Yi, et al.
Published: (2024)

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law
by: Kunstner, Frederik, 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)

Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere?
by: Meng, Si Yi, et al.
Published: (2025)

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

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

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

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

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)

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

Functional Central Limit Theorem for Stochastic Gradient Descent
by: Flamand, Kessang, et al.
Published: (2026)

Open Problem: Anytime Convergence Rate of Gradient Descent
by: Kornowski, Guy, et al.
Published: (2024)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2024)

Parameter-free Clipped Gradient Descent Meets Polyak
by: Takezawa, Yuki, et al.
Published: (2024)