Cover Image

Convergence Rate in Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Zixi, Xu, Yumin, Zhang, Ruixun
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

Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

Scalable Bi-causal Optimal Transport via KL Relaxation and Policy Gradients
by: Cao, Haoyang, et al.
Published: (2026)

Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving $O(1/k)$ Finite-Sample Complexity
by: Doan, Thinh T.
Published: (2024)

Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
by: Haque, Shaan Ul, et al.
Published: (2023)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

$O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation
by: Chandak, Siddharth
Published: (2025)

Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise
by: Chandak, Siddharth, et al.
Published: (2025)

Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)

Central Limit Theorem for Two-Time-Scale Approximate Distributionally Robust RL
by: Wang, Shengbo, et al.
Published: (2026)

Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
by: Liu, Xinyu, et al.
Published: (2026)

Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
by: Chandak, Siddharth, et al.
Published: (2026)

Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning
by: Zeng, Sihan, et al.
Published: (2024)

A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
by: Zeng, Sihan, et al.
Published: (2021)

Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)

Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
by: Grazzi, Riccardo, et al.
Published: (2024)

Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization
by: Gong, Xiaochuan, et al.
Published: (2025)

Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
by: Zhu, Feng, et al.
Published: (2025)

The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
by: Huo, Dongyan, et al.
Published: (2024)

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
by: Adibi, Arman, et al.
Published: (2024)

Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
by: Yu, Yaxin, et al.
Published: (2026)

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
by: Kwon, Jeongyeol, et al.
Published: (2024)

Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence
by: Bonalli, Riccardo, et al.
Published: (2023)

Optimal Local Convergence Rates of Stochastic First-Order Methods under Local $α$-PL
by: Masiha, Saeed, et al.
Published: (2024)

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

A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization
by: Chu, Tianshu, et al.
Published: (2025)

Convergence Rate Analysis of LION
by: Dong, Yiming, et al.
Published: (2024)

Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
by: Hu, Jie, et al.
Published: (2024)

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
by: Han, Yinbin, et al.
Published: (2024)

Convergence Rate Analysis of the AdamW-Style Shampoo: Unifying One-Sided and Two-Sided Preconditioning
by: Li, Huan, et al.
Published: (2026)

Tame Riemannian Stochastic Approximation
by: Aspman, Johannes, et al.
Published: (2023)

Exponential Concentration in Stochastic Approximation
by: Law, Kody, et al.
Published: (2022)

Contextual Stochastic Vehicle Routing with Time Windows
by: Serrano, Breno, et al.
Published: (2024)

On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
by: Kong, Boao, et al.
Published: (2026)

Concentration of Contractive Stochastic Approximation: Additive and Multiplicative Noise
by: Chen, Zaiwei, et al.
Published: (2023)

Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
by: Liu, Yuxing, et al.
Published: (2025)

Weakly Time-Coupled Approximation of Markov Decision Processes
by: Soheili, Negar, et al.
Published: (2026)

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
by: Levin, Ilya, et al.
Published: (2026)

Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
by: Huo, Dongyan, et al.
Published: (2022)

Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
by: Zhang, Siyuan, et al.
Published: (2024)

Convergence Rates for Gradient Descent on the Edge of Stability in Overparametrised Least Squares
by: MacDonald, Lachlan Ewen, et al.
Published: (2025)