:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Lei, Jinlong, Shanbhag, Uday V.
Formato:	Preprint
Publicado:	2020
Materias:	Optimization and Control
Acceso en línea:	https://arxiv.org/abs/2006.07769
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Distributed Iterative Tikhonov Method for Networked Monotone Aggregative Hierarchical Stochastic Games
por: Lei, Jinlong, et al.
Publicado: (2023)

Zeroth-order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization
por: Marrinan, Luke, et al.
Publicado: (2023)

Complexity Guarantees for Zeroth-order Methods via Exponentially-shifted Gaussian Smoothing: Mitigating Dimension-dependence and Incorporating Decision-dependence
por: Wang, Mingrui, et al.
Publicado: (2026)

Zeroth-Order Federated Methods for Stochastic MPECs and Nondifferentiable Nonconvex Hierarchical Optimization
por: Qiu, Yuyang, et al.
Publicado: (2023)

Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs
por: Ebrahimi, Mohammadjavad, et al.
Publicado: (2023)

A Parameter-Free Stochastic LineseArch Method (SLAM) for Minimizing Expectation Residuals
por: Wang, Qi, et al.
Publicado: (2025)

On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods
por: Ebrahimi, Mohammadjavad, et al.
Publicado: (2025)

Equilibrium Invariance, Proximality, and Surrogation: Moreau-Smoothed Best-Response Pathways in Stochastic Nonsmooth Games
por: Xiao, Zhuoyu, et al.
Publicado: (2026)

Computing Equilibria in Stochastic Nonconvex and Non-monotone Games via Gradient-Response Schemes
por: Xiao, Zhuoyu, et al.
Publicado: (2025)

On the Sampling-based Computation of Nash Equilibria under Uncertainty via the Nikaido-Isoda Function
por: Marrinan, Luke, et al.
Publicado: (2025)

Complexity guarantees for risk-neutral generalized Nash equilibrium problems
por: Tao, Haochen, et al.
Publicado: (2025)

A regularized variance-reduced modified extragradient method for stochastic hierarchical games
por: Cui, Shisheng, et al.
Publicado: (2023)

Some Unified Theory for Variance Reduced Prox-Linear Methods
por: Wu, Yue, et al.
Publicado: (2024)

An Accelerated Variance Reduced Extra-Point Approach to Finite-Sum VI and Optimization
por: Huang, Kevin, et al.
Publicado: (2022)

Revisit First-order Methods for Geodesically Convex Optimization
por: Shu, Yunlu, et al.
Publicado: (2025)

Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks
por: Patel, Laksh, et al.
Publicado: (2025)

Central Limit Theorems for Asynchronous Averaged Q-Learning
por: Liu, Xingtu
Publicado: (2025)

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

Variance Reduced Random Relaxed Projection Method for Constrained Finite-sum Minimization Problems
por: Yang, Zhichun, et al.
Publicado: (2022)

A Block Coordinate and Variance-Reduced Method for Generalized Variational Inequalities of Minty Type
por: Diakonikolas, Jelena
Publicado: (2024)

Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
por: Dereziński, Michał
Publicado: (2022)

Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
por: Upadhyay, Antesh, et al.
Publicado: (2026)

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
por: Wang, Lei, et al.
Publicado: (2022)

Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
por: Chayti, El Mahdi, et al.
Publicado: (2023)

Breaking the Stochasticity Barrier: An Adaptive Variance-Reduced Method for Variational Inequalities
por: Jeong, Yungi, et al.
Publicado: (2026)

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
por: Liang, Ling, et al.
Publicado: (2024)

Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
por: Milz, Johannes, et al.
Publicado: (2025)

Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
por: Srikant, R.
Publicado: (2024)

Improved Rates for Stochastic Variance-Reduced Difference-of-Convex Algorithms
por: Nguyen, Anh Duc, et al.
Publicado: (2025)

A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles
por: Kerger, Phillip, et al.
Publicado: (2024)

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

Alternating Stochastic Variance-Reduced Algorithms with Optimal Complexity for Bilevel Optimization
por: Huo, Haimei, et al.
Publicado: (2024)

VFOG: Variance-Reduced Fast Optimistic Gradient Methods for a Class of Nonmonotone Generalized Equations
por: Tran-Dinh, Quoc, et al.
Publicado: (2025)

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

Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
por: Boveiri, Mohammad, et al.
Publicado: (2024)

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

Variance-Reduced Gradient Estimator for Nonconvex Zeroth-Order Distributed Optimization
por: Mu, Huaiyi, et al.
Publicado: (2024)

Distributed Riemannian Stochastic Gradient Tracking Algorithm on the Stiefel Manifold
por: Zhao, Jishu, et al.
Publicado: (2024)

Split, Skip and Play: Variance-Reduced ProxSkip for Tomography Reconstruction is Extremely Fast
por: Papoutsellis, Evangelos, et al.
Publicado: (2026)

Divergence Results and Convergence of a Variance Reduced Version of ADAM
por: Wang, Ruiqi, et al.
Publicado: (2022)