Convergence of two-timescale gradient descent ascent dynamics: finite-dimensional and mean-field perspectives
Fuente:
arXiv
Saved in:
| Main Authors: | An, Jing, Lu, Jianfeng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024)
by: Stollenwerk, Alexander, et al.
Published: (2024)
Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024)
by: Dereich, Steffen, et al.
Published: (2024)
Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks
by: An, Jing, et al.
Published: (2023)
by: An, Jing, et al.
Published: (2023)
Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics
by: Nakano, Yumiharu
Published: (2026)
by: Nakano, Yumiharu
Published: (2026)
First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
by: Lu, Zhaosong, et al.
Published: (2025)
by: Lu, Zhaosong, 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)
Non-Euclidean dual gradient ascent for entropically regularized linear and semidefinite programming
by: Cai, Yuhang, et al.
Published: (2025)
by: Cai, Yuhang, et al.
Published: (2025)
Natural Riemannian gradient for learning functional tensor networks
by: Klug, Nikolas, et al.
Published: (2026)
by: Klug, Nikolas, et al.
Published: (2026)
Quantitative Convergences of Lie Group Momentum Optimizers
by: Kong, Lingkai, et al.
Published: (2024)
by: Kong, Lingkai, et al.
Published: (2024)
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
by: Häusner, Paul, et al.
Published: (2023)
by: Häusner, Paul, et al.
Published: (2023)
Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size
by: Dereziński, Michał, et al.
Published: (2026)
by: Dereziński, Michał, et al.
Published: (2026)
Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)
by: Lim, Dong-Young, et al.
Published: (2022)
Anderson Acceleration in Nonsmooth Problems: Local Convergence via Active Manifold Identification
by: Li, Kexin, et al.
Published: (2024)
by: Li, Kexin, et al.
Published: (2024)
Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case
by: He, Meixuan, et al.
Published: (2023)
by: He, Meixuan, et al.
Published: (2023)
A perturbed preconditioned gradient descent method for the unconstrained minimization of composite objectives
by: Park, Jea-Hyun, et al.
Published: (2025)
by: Park, Jea-Hyun, et al.
Published: (2025)
Fully discretized Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem
by: Chen, Ziang, et al.
Published: (2024)
by: Chen, Ziang, et al.
Published: (2024)
Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
by: Liang, Luxu, et al.
Published: (2024)
by: Liang, Luxu, et al.
Published: (2024)
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)
by: Tomasetto, Matteo, et al.
Published: (2024)
Convergence of a steepest descent algorithm in shape optimisation using $W^{1,\infty}$ functions
by: Deckelnick, Klaus, et al.
Published: (2023)
by: Deckelnick, Klaus, et al.
Published: (2023)
Travel-time tomography from mean field game dynamics
by: Xu, Longqiang, et al.
Published: (2026)
by: Xu, Longqiang, et al.
Published: (2026)
On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
by: Xu, Ruihan, et al.
Published: (2026)
by: Xu, Ruihan, et al.
Published: (2026)
Convergence of a particle method for gradient flows on the $L^p$-Wasserstein space
by: Lei, Rong
Published: (2025)
by: Lei, Rong
Published: (2025)
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)
by: Cheng, Xiuyuan, et al.
Published: (2023)
Computational analysis on a linkage between generalized logit dynamic and discounted mean field game
by: Yoshioka, Hidekazu
Published: (2024)
by: Yoshioka, Hidekazu
Published: (2024)
Convergence Analysis of Fractional Gradient Descent
by: Aggarwal, Ashwani
Published: (2023)
by: Aggarwal, Ashwani
Published: (2023)
Randomized Kaczmarz Methods with Beyond-Krylov Convergence
by: Dereziński, Michał, et al.
Published: (2025)
by: Dereziński, Michał, et al.
Published: (2025)
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
by: Niu, Chengmei, et al.
Published: (2025)
by: Niu, Chengmei, et al.
Published: (2025)
A Family of Controllable Momentum Coefficients for Forward-Backward Accelerated Algorithms
by: Fu, Mingwei, et al.
Published: (2025)
by: Fu, Mingwei, et al.
Published: (2025)
Control, Optimal Transport and Neural Differential Equations in Supervised Learning
by: Phung, Minh-Nhat, et al.
Published: (2025)
by: Phung, Minh-Nhat, et al.
Published: (2025)
AutoBalance: An Automatic Balancing Framework for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2025)
by: An, Kang, et al.
Published: (2025)
Minimisation of Submodular Functions Using Gaussian Zeroth-Order Random Oracles
by: Farzin, Amir Ali, et al.
Published: (2025)
by: Farzin, Amir Ali, et al.
Published: (2025)
Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
by: Long, Luo, et al.
Published: (2025)
by: Long, Luo, et al.
Published: (2025)
Learning Regularization Functionals for Inverse Problems: A Comparative Study
by: Hertrich, Johannes, et al.
Published: (2025)
by: Hertrich, Johannes, et al.
Published: (2025)
Machine Learning and Control: Foundations, Advances, and Perspectives
by: Zuazua, Enrique
Published: (2025)
by: Zuazua, Enrique
Published: (2025)
Policy Gradient with Second Order Momentum
by: Sun, Tianyu
Published: (2025)
by: Sun, Tianyu
Published: (2025)
sparseGeoHOPCA: A Geometric Solution to Sparse Higher-Order PCA Without Covariance Estimation
by: Xu, Renjie, et al.
Published: (2025)
by: Xu, Renjie, et al.
Published: (2025)
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)
Accuracy of Discretely Sampled Stochastic Policies in Continuous-time Reinforcement Learning
by: Jia, Yanwei, et al.
Published: (2025)
by: Jia, Yanwei, et al.
Published: (2025)
Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
by: Tyurin, Alexander
Published: (2025)
by: Tyurin, Alexander
Published: (2025)
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
by: Jentzen, Arnulf, et al.
Published: (2025)
by: Jentzen, Arnulf, et al.
Published: (2025)
Similar Items
-
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024) -
Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024) -
Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks
by: An, Jing, et al.
Published: (2023) -
Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics
by: Nakano, Yumiharu
Published: (2026) -
First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
by: Lu, Zhaosong, et al.
Published: (2025)