Cover Image

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Shu, Osher, Stanley, Li, Wuchen
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Numerical Analysis on Neural Network Projected Schemes for Approximating One Dimensional Wasserstein Gradient Flows
by: Zuo, Xinzhe, et al.
Published: (2024)

A Primal-dual hybrid gradient method for solving optimal control problems and the corresponding Hamilton-Jacobi PDEs
by: Meng, Tingwei, et al.
Published: (2024)

Gradient-adjusted underdamped Langevin dynamics for sampling
by: Zuo, Xinzhe, et al.
Published: (2024)

Numerical analysis of a first-order computational algorithm for reaction-diffusion equations via the primal-dual hybrid gradient method
by: Liu, Shu, et al.
Published: (2024)

Tensor train based sampling algorithms for approximating regularized Wasserstein proximal operators
by: Han, Fuqun, et al.
Published: (2024)

Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)

Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)

Control, Optimal Transport and Neural Differential Equations in Supervised Learning
by: Phung, Minh-Nhat, et al.
Published: (2025)

Score-based Neural Ordinary Differential Equations for Computing Mean Field Control Problems
by: Zhou, Mo, et al.
Published: (2024)

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
by: Dai, Yongcheng, et al.
Published: (2023)

Accelerated Primal-Dual Proximal Gradient Splitting Methods for Convex-Concave Saddle-Point Problems
by: Luo, Hao
Published: (2024)

Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations
by: Heredia, Carlos
Published: (2024)

Adaptive Proximal Gradient Method for Convex Optimization
by: Malitsky, Yura, et al.
Published: (2023)

AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2025)

Sparse Transformer Architectures via Regularized Wasserstein Proximal Operator with $L_1$ Prior
by: Han, Fuqun, et al.
Published: (2025)

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
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)

Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent
by: Bian, Fengmiao, et al.
Published: (2025)

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
by: Mishra, Neel, et al.
Published: (2024)

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)

Expansive Natural Neural Gradient Flows for Energy Minimization
by: Dahmen, Wolfgang, et al.
Published: (2025)

KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems
by: Aghaei, Alireza Afzal
Published: (2024)

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

Policy Gradient with Second Order Momentum
by: Sun, Tianyu
Published: (2025)

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)

A Primal-dual Forward-backward Splitting Method for Cross-diffusion Gradient Flows with General Mobility Matrices
by: Deng, Yunhong, et al.
Published: (2025)

Provably Faster Gradient Descent via Long Steps
by: Grimmer, Benjamin
Published: (2023)

Fine-Tune Language Models as Multi-Modal Differential Equation Solvers
by: Yang, Liu, et al.
Published: (2023)

A Generalized Primal-Dual Correction Method for Saddle-Point Problems with a Nonlinear Coupling Operator
by: Wang, Sai, et al.
Published: (2023)

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
by: Huang, Feihu, et al.
Published: (2023)

Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
by: Tyurin, Alexander
Published: (2025)

Computational Control of Nonlinear Partial Differential Equations Using Machine Learning
by: Kurbanov, Maximilian, et al.
Published: (2026)

Stochastic Langevin Differential Inclusions with Applications to Machine Learning
by: Difonzo, Fabio V., et al.
Published: (2022)

PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training
by: Yang, Shenghao, et al.
Published: (2026)

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
by: Shen, Weiping, et al.
Published: (2026)

Acceleration Methods
by: d'Aspremont, Alexandre, et al.
Published: (2021)

Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach
by: Farzin, Amir Ali, et al.
Published: (2026)

Natural Riemannian gradient for learning functional tensor networks
by: Klug, Nikolas, et al.
Published: (2026)

Inexact Newton Methods for Solving Generalized Equations on Riemannian Manifolds
by: Louzeiro, Mauricio S., et al.
Published: (2023)

WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
by: Li, Dongyue, et al.
Published: (2026)