Imagen de Portada

Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Liu, Shixin, Gao, Ming, Hu, Jian
Formato:	Preprint
Publicado:	2024
Materias:	Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Bayesian Optimization with Lower Confidence Bounds for Minimization Problems with Known Outer Structure
por: Baumgärtner, Katrin, et al.
Publicado: (2025)

Contextual Quantile Minimization for Two-Stage Stochastic Programs
por: Tsang, Man Yiu, et al.
Publicado: (2025)

Heuristic Bundle Upper Bound Based Polyhedral Bundle Method for Semidefinite Programming
por: Cui, Zilong, et al.
Publicado: (2025)

Problem-Driven Scenario Reduction Framework for Power System Stochastic Operation
por: Zhuang, Yingrui, et al.
Publicado: (2024)

Robust Confidence Bands for Stochastic Processes Using Simulation
por: Chan, Timothy, et al.
Publicado: (2024)

On Data-Driven Stochastic Output-Feedback Predictive Control
por: Pan, Guanru, et al.
Publicado: (2022)

An Iterative Problem-Driven Scenario Reduction Framework for Stochastic Optimization with Conditional Value-at-Risk
por: Zhuang, Yingrui, et al.
Publicado: (2025)

Data-driven Policies For Two-stage Stochastic Linear Programs
por: Sharma, Chhavi, et al.
Publicado: (2026)

Controlling Microgrids Without External Data: A Benchmark of Stochastic Programming Methods
por: Puech, Alban, et al.
Publicado: (2023)

Global Convergence and Error Propagation in Neural Gradient Flows: A Riemannian Optimization Framework
por: Zheng, Shixin, et al.
Publicado: (2026)

DUST: A Framework for Data-Driven Density Steering
por: Pilipovsky, Joshua, et al.
Publicado: (2024)

Lower Bounds for Linear Minimization Oracle Methods Optimizing over Strongly Convex Sets
por: Grimmer, Benjamin, et al.
Publicado: (2026)

A Nationwide Multi-Location Multi-Resource Stochastic Programming Based Energy Planning Framework
por: Al-Lawati, Razan A. H., et al.
Publicado: (2023)

Data-Driven Superstabilizing Control under Quadratically-Bounded Errors-in-Variables Noise
por: Miller, Jared, et al.
Publicado: (2024)

The Power of Simple Menus in Robust Selling Mechanisms
por: Wang, Shixin
Publicado: (2023)

A Stochastic Conjugate Subgradient Algorithm for Two-stage Stochastic Programming
por: Zhang, Di, et al.
Publicado: (2025)

Noise Sensitivity of the Semidefinite Programs for Direct Data-Driven LQR
por: Zeng, Xiong, et al.
Publicado: (2024)

Convex Data-Driven Contraction With Riemannian Metrics
por: Oliveira, Andreas, et al.
Publicado: (2024)

Data-Driven Stochastic Distribution System Hardening Based on Bayesian Online Learning
por: Shi, Wenlong, et al.
Publicado: (2025)

Sample Complexity of Data-driven Multistage Stochastic Programming under Markovian Uncertainty
por: Park, Hyuk, et al.
Publicado: (2024)

Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
por: Jiang, Ruichen, et al.
Publicado: (2024)

Adaptive Multistage Stochastic Programming
por: Kayacık, Sezen Ece, et al.
Publicado: (2024)

Probing-Enhanced Stochastic Programming
por: Ma, Zhichao, et al.
Publicado: (2024)

Stochastic Quadratic Dynamic Programming
por: Guigues, Vincent, et al.
Publicado: (2025)

An Optimization-Based Framework for Solving Forward-Backward Stochastic Differential Equations: Convergence Analysis and Error Bounds
por: Wang, Yutian, et al.
Publicado: (2025)

A Versatile Framework for Data-Driven Control of Nonlinear Systems
por: Monshizadeh, Nima, et al.
Publicado: (2024)

Data-Driven Output Prediction and Control of Stochastic Systems: An Innovation-Based Approach
por: Wang, Yibo, et al.
Publicado: (2022)

Robust Data-Driven Quasiconcave Optimization
por: Wu, Jian, et al.
Publicado: (2025)

From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
por: Balim, Haldun, et al.
Publicado: (2024)

Large Deviations Analysis For Regret Minimizing Stochastic Approximation Algorithms
por: Qian, Hongjiang, et al.
Publicado: (2024)

Data-Driven Linear Quadratic Control Using Output-Feedback via Non-Minimal Realization
por: Li, Weijian, et al.
Publicado: (2026)

LOTUS: A Warm-Start Framework for Powering Dual Decomposition in Large-Scale Two-Stage Stochastic Programs
por: Cornielje, Emma, et al.
Publicado: (2026)

Robust Data-Driven Receding Horizon Control
por: Zheng, Jian, et al.
Publicado: (2025)

Robust Data-Driven Receding-Horizon Control for LQR with Input Constraints
por: Zheng, Jian, et al.
Publicado: (2025)

An Error Bound for Aggregation in Approximate Dynamic Programming
por: Li, Yuchao, et al.
Publicado: (2025)

Performance Bounds for Rollout Policies in Stochastic Shortest Path Problems
por: Hansson, Anders, et al.
Publicado: (2026)

Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors
por: Balim, Haldun, et al.
Publicado: (2024)

Lower and Upper Bounds for Small Canonical and Ordered Ramsey Numbers
por: Brosch, Daniel, et al.
Publicado: (2025)

Strength of the Upper Bounds for the Edge-Weighted Maximum Clique Problem
por: Ciccarelli, Fabio, et al.
Publicado: (2025)

Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information
por: Zhang, Yiyang, et al.
Publicado: (2023)