Copertina

Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ebrahimi, Mohammadjavad, Shanbhag, Uday V., Yousefian, Farzad
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

Self-tuned Regularized Federated Methods with Guarantees for Optimal Solution Selection
di: Ebrahimi, Mohammadjavad, et al.
Pubblicazione: (2025)

On Performance Guarantees for Federated Learning with Personalized Constraints
di: Ebrahimi, Mohammadjavad, et al.
Pubblicazione: (2026)

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

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

Iteratively Regularized Gradient Tracking Methods for Optimal Equilibrium Seeking
di: Qiu, Yuyang, et al.
Pubblicazione: (2024)

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

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

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

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

Variance-Reduced Accelerated First-order Methods: Central Limit Theorems and Confidence Statements
di: Lei, Jinlong, et al.
Pubblicazione: (2020)

Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation
di: Maleki, Fateme, et al.
Pubblicazione: (2026)

Improved guarantees for optimal Nash equilibrium seeking and bilevel variational inequalities
di: Samadi, Sepideh, et al.
Pubblicazione: (2023)

On iteratively regularized first-order methods for simple bilevel optimization
di: Samadi, Sepideh, et al.
Pubblicazione: (2025)

A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning
di: Qiu, Yuyang, et al.
Pubblicazione: (2025)

Distributed Adaptive Gradient Algorithm with Gradient Tracking for Stochastic Non-Convex Optimization
di: Han, Dongyu, et al.
Pubblicazione: (2024)

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

S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization
di: Li, Huaqing, et al.
Pubblicazione: (2019)

Introduction to Mathematical Programming with Equilibrium Constraints (MPECs) and Bilevel Optimization
di: Wang, Louis Shuo
Pubblicazione: (2026)

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

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

Accelerated Alternating Direction Method of Multipliers Gradient Tracking for Distributed Optimization
di: Sebastián, Eduardo, et al.
Pubblicazione: (2024)

High-Probability Guarantees for Random Zeroth-Order (Stochastic) Gradient Descent
di: Ye, Haishan
Pubblicazione: (2026)

Optimization Workshop Notes for Mathematical Programming with Equilibrium Constraints (MPECs): Verification of MPEC Hypotheses
di: Yu, Jiguang
Pubblicazione: (2026)

Distributed Normal Map-based Stochastic Proximal Gradient Methods over Networks
di: Huang, Kun, et al.
Pubblicazione: (2024)

Clipped Stochastic Gradient Tracking For Locally Smooth Functions
di: Mei, Leilei, et al.
Pubblicazione: (2026)

Optimization Workshop Notes for Mathematical Programming with Equilibrium Constraints (MPECs): Second-Order Optimality Conditions
di: Yu, Jiguang
Pubblicazione: (2026)

Black-Box Followers, White-Box Leaders: Partial Zeroth-Order Methods for MPECs
di: Fischer, Miriam, et al.
Pubblicazione: (2026)

Convergence of the Iterates of the Stochastic Proximal Gradient Method
di: Madariaga, Javier I.
Pubblicazione: (2026)

Universal Gradient Methods for Stochastic Convex Optimization
di: Rodomanov, Anton, et al.
Pubblicazione: (2024)

Riemannian Inexact Gradient Descent for Quadratic Discrimination
di: Talwar, Uday, et al.
Pubblicazione: (2025)

Negative Curvature Methods with High-Probability Complexity Guarantees for Stochastic Nonconvex Optimization
di: Berahas, Albert S., et al.
Pubblicazione: (2026)

Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization
di: Xu, Lei, et al.
Pubblicazione: (2023)

A Weighted Gradient Tracking Privacy-Preserving Method for Distributed Optimization
di: Xie, Furan, et al.
Pubblicazione: (2025)

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

Nonlinearly Preconditioned Gradient Methods: Momentum and Stochastic Analysis
di: Oikonomidis, Konstantinos, et al.
Pubblicazione: (2025)

Event-triggered Dual Gradient Tracking for Distributed Resource Allocation
di: Xu, Xiayan, et al.
Pubblicazione: (2025)

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
di: Huang, Yan, et al.
Pubblicazione: (2025)