Super Gradient Descent: Global Optimization requires Global Gradient
Fuente:
arXiv
Saved in:
| Main Author: | Achour, Seifeddine |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Provably Faster Gradient Descent via Long Steps
by: Grimmer, Benjamin
Published: (2023)
by: Grimmer, Benjamin
Published: (2023)
Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
by: Tyurin, Alexander
Published: (2025)
by: Tyurin, Alexander
Published: (2025)
Modified Loss of Momentum Gradient Descent: Fine-Grained Analysis
by: Cattaneo, Matias D., et al.
Published: (2025)
by: Cattaneo, Matias D., et al.
Published: (2025)
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)
by: Hwang, Youngsik, et al.
Published: (2024)
Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent
by: Bian, Fengmiao, et al.
Published: (2025)
by: Bian, Fengmiao, et al.
Published: (2025)
On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
by: Xia, Lu, et al.
Published: (2023)
by: Xia, Lu, et al.
Published: (2023)
Adaptive Proximal Gradient Method for Convex Optimization
by: Malitsky, Yura, et al.
Published: (2023)
by: Malitsky, Yura, et al.
Published: (2023)
How to unlearn a learned Machine Learning model ?
by: Achour, Seifeddine
Published: (2024)
by: Achour, Seifeddine
Published: (2024)
Convergence Analysis of Fractional Gradient Descent
by: Aggarwal, Ashwani
Published: (2023)
by: Aggarwal, Ashwani
Published: (2023)
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
by: Lu, Songtao
Published: (2022)
by: Lu, Songtao
Published: (2022)
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
by: Huang, Feihu, et al.
Published: (2023)
by: Huang, Feihu, et al.
Published: (2023)
Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
by: O'Leary-Roseberry, Thomas, et al.
Published: (2024)
by: O'Leary-Roseberry, Thomas, et al.
Published: (2024)
Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
by: Yuan, Ganzhao
Published: (2026)
by: Yuan, Ganzhao
Published: (2026)
Policy Gradient with Second Order Momentum
by: Sun, Tianyu
Published: (2025)
by: Sun, Tianyu
Published: (2025)
A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2023)
by: Yuan, Ganzhao
Published: (2023)
Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
by: Shen, Weiping, et al.
Published: (2026)
by: Shen, Weiping, et al.
Published: (2026)
Global Convergence and Error Propagation in Neural Gradient Flows: A Riemannian Optimization Framework
by: Zheng, Shixin, et al.
Published: (2026)
by: Zheng, Shixin, et al.
Published: (2026)
Dimensionality Reduction Techniques for Global Bayesian Optimisation
by: Long, Luo, et al.
Published: (2024)
by: Long, Luo, et al.
Published: (2024)
IRKA is a Riemannian Gradient Descent Method
by: Mlinarić, Petar, et al.
Published: (2023)
by: Mlinarić, Petar, et al.
Published: (2023)
A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
by: Liu, Shu, et al.
Published: (2024)
by: Liu, Shu, et al.
Published: (2024)
Low-Discrepancy Set Post-Processing via Gradient Descent
by: Clément, François, et al.
Published: (2025)
by: Clément, François, et al.
Published: (2025)
Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares
by: Peng, Liangzu, et al.
Published: (2024)
by: Peng, Liangzu, et al.
Published: (2024)
The Essential Best and Average Rate of Convergence of the Exact Line Search Gradient Descent Method
by: Yu, Thomas
Published: (2023)
by: Yu, Thomas
Published: (2023)
Multilevel Stochastic Gradient Descent for Optimal Control Under Uncertainty
by: Baumgarten, Niklas, et al.
Published: (2025)
by: Baumgarten, Niklas, et al.
Published: (2025)
Randomised Splitting Methods and Stochastic Gradient Descent
by: Shaw, Luke, et al.
Published: (2025)
by: Shaw, Luke, et al.
Published: (2025)
Quantitative Convergences of Lie Group Momentum Optimizers
by: Kong, Lingkai, et al.
Published: (2024)
by: Kong, Lingkai, et al.
Published: (2024)
Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
by: Long, Luo, et al.
Published: (2025)
by: Long, Luo, et al.
Published: (2025)
Scalable Acceleration for Classification-Based Derivative-Free Optimization
by: Han, Tianyi, et al.
Published: (2023)
by: Han, Tianyi, et al.
Published: (2023)
Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)
by: Zhu, Linglingzhi, et al.
Published: (2026)
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
by: Mishra, Neel, et al.
Published: (2024)
by: Mishra, Neel, et al.
Published: (2024)
Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
by: Gong, Xindi, et al.
Published: (2026)
by: Gong, Xindi, et al.
Published: (2026)
UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization
by: Jiang, Yiming, et al.
Published: (2023)
by: Jiang, Yiming, et al.
Published: (2023)
OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
by: Yuan, Ganzhao
Published: (2026)
by: Yuan, Ganzhao
Published: (2026)
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
by: Ma, Shaocong, et al.
Published: (2024)
by: Ma, Shaocong, et al.
Published: (2024)
Interpolation-Based Gradient-Error Bounds for Use in Derivative-Free Optimization of Noisy Functions
by: Marchetti, Alejandro G., et al.
Published: (2025)
by: Marchetti, Alejandro G., et al.
Published: (2025)
Bayesian Optimization on Networks
by: Li, Wenwen, et al.
Published: (2025)
by: Li, Wenwen, et al.
Published: (2025)
An Abstract Lyapunov Control Optimizer: Local Stabilization and Global Convergence
by: Bensaid, Bilel, et al.
Published: (2024)
by: Bensaid, Bilel, et al.
Published: (2024)
Gradient-adjusted underdamped Langevin dynamics for sampling
by: Zuo, Xinzhe, et al.
Published: (2024)
by: Zuo, Xinzhe, et al.
Published: (2024)
PowerStep: Memory-Efficient Adaptive Optimization via $\ell_p$-Norm Steepest Descent
by: Lu, Yao, et al.
Published: (2026)
by: Lu, Yao, et al.
Published: (2026)
Similar Items
-
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) -
Provably Faster Gradient Descent via Long Steps
by: Grimmer, Benjamin
Published: (2023) -
Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
by: Tyurin, Alexander
Published: (2025) -
Modified Loss of Momentum Gradient Descent: Fine-Grained Analysis
by: Cattaneo, Matias D., et al.
Published: (2025) -
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)