A comparison of global and semi-local approximation in \(T\)-stage stochastic optimization

DOI10.1016/J.EJOR.2010.08.002zbMath1208.90123OpenAlexW2025003926MaRDI QIDQ621672

Danilo Macciò, Cristiano Cervellera

Publication date: 28 January 2011

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2010.08.002

zbMATH Keywords

dynamic programmingneural networksMarkov processes

Mathematics Subject Classification ID

Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (4)

Convergence of controlled models and finite-state approximation for discounted continuous-time Markov decision processes with constraints ⋮ Low-discrepancy sampling for approximate dynamic programming with local approximators ⋮ Approximate stochastic dynamic programming for hydroelectric production planning ⋮ Inventory -- forecasting: mind the gap

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Management of water resource systems in the presence of uncertainties by nonlinear approximation techniques and deterministic sampling
• Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization
• Neural network and regression spline value function approximations for stochastic dynamic programming
• Efficient sampling in approximate dynamic programming algorithms
• Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming
• Functional Optimization Through Semilocal Approximate Minimization
• Numerical Solution of Continuous-State Dynamic Programs Using Linear and Spline Interpolation
• Real Analysis and Probability
• On the distribution of points in a cube and the approximate evaluation of integrals
• Polynomial Approximation--A New Computational Technique in Dynamic Programming: Allocation Processes
• Controlling multi-reservoir systems.
• The elements of statistical learning. Data mining, inference, and prediction
• Approximating networks and extended Ritz method for the solution of functional optimization problems

This page was built for publication: A comparison of global and semi-local approximation in \(T\)-stage stochastic optimization