Image de couverture de livre

A Gradient Guided Diffusion Framework for Chance Constrained Programming

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhang, Boyang, Wang, Zhiguo, Liu, Ya-Feng
Format:	Preprint
Publié:	2025
Sujets:	Optimization and Control Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Proximal-Based Generative Modeling for Bayesian Inverse Problems
par: Zhang, Boyang, et autres
Publié: (2026)

Conformal Predictive Programming for Chance Constrained Optimization
par: Zhao, Yiqi, et autres
Publié: (2024)

On Learning for Ambiguous Chance Constrained Problems
par: Madhusudanarao, A Ch, et autres
Publié: (2023)

Flipping-based Policy for Chance-Constrained Markov Decision Processes
par: Shen, Xun, et autres
Publié: (2024)

Adaptive Learning-based Surrogate Method for Stochastic Programs with Implicitly Decision-dependent Uncertainty
par: Shen, Boyang, et autres
Publié: (2025)

Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion
par: Zeng, Hao, et autres
Publié: (2024)

Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
par: Kato, Teruki, et autres
Publié: (2026)

Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
par: Ruan, Boyuan, et autres
Publié: (2026)

Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
par: Ding, Dongsheng, et autres
Publié: (2023)

Feed m Birds with One Scone: Accelerating Multi-task Gradient Balancing via Bi-level Optimization
par: Chen, Xuxing, et autres
Publié: (2026)

A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
par: Curtis, Frank E., et autres
Publié: (2023)

Gradient Methods with Online Scaling
par: Gao, Wenzhi, et autres
Publié: (2024)

Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
par: Chiang, John
Publié: (2022)

Gradient Methods with Online Scaling Part I. Theoretical Foundations
par: Gao, Wenzhi, et autres
Publié: (2025)

Gradient Methods with Online Scaling Part II. Practical Aspects
par: Chu, Ya-Chi, et autres
Publié: (2025)

Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
par: Richtárik, Peter, et autres
Publié: (2026)

Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
par: Zeng, Sihan, et autres
Publié: (2024)

A First-order Generative Bilevel Optimization Framework for Diffusion Models
par: Xiao, Quan, et autres
Publié: (2025)

Convex Chance-Constrained Programs with Wasserstein Ambiguity
par: Shen, Haoming, et autres
Publié: (2021)

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
par: Na, Sen, et autres
Publié: (2022)

Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach
par: Nguyen, Hoang Huy, et autres
Publié: (2024)

Projected Forward Gradient-Guided Frank-Wolfe Algorithm via Variance Reduction
par: Rostami, M., et autres
Publié: (2024)

Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
par: Pan, Rui, et autres
Publié: (2023)

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
par: Chakrabarti, Kushal, et autres
Publié: (2022)

Inverse Chance Constrained Optimal Power Flow
par: Wang, Shenglu, et autres
Publié: (2025)

A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization
par: Berahas, Albert S., et autres
Publié: (2023)

ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
par: Huang, Weimin, et autres
Publié: (2026)

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
par: Wang, Lei, et autres
Publié: (2022)

An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
par: Yang, Linxin, et autres
Publié: (2024)

Natural Policy Gradient as Doubly Smoothed Policy Iteration: A Bellman-Operator Framework
par: Nanda, Phalguni, et autres
Publié: (2026)

The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach
par: Song, Yongcun, et autres
Publié: (2023)

ASGO: Adaptive Structured Gradient Optimization
par: An, Kang, et autres
Publié: (2025)

Gradient-Variation Online Learning under Generalized Smoothness
par: Xie, Yan-Feng, et autres
Publié: (2024)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
par: Ziyin, Liu, et autres
Publié: (2024)

First-Order Methods for Linearly Constrained Bilevel Optimization
par: Kornowski, Guy, et autres
Publié: (2024)

Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
par: Li, Zihao, et autres
Publié: (2024)

A Concise Lyapunov Analysis of Nesterov's Accelerated Gradient Method
par: Liu, Jun
Publié: (2025)

FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning
par: Chen, Lisha, et autres
Publié: (2024)

A Proximal DC Algorithm for Sample Average Approximation of Chance Constrained Programming
par: Wang, Peng, et autres
Publié: (2023)

GANs as Gradient Flows that Converge
par: Huang, Yu-Jui, et autres
Publié: (2022)