Relaxations of Weakly Coupled Stochastic Dynamic Programs

We consider a broad class of stochastic dynamic programming problems that are amenable to relaxation via decomposition. These problems comprise multiple subproblems that are independent of each other except for a collection of coupling constraints on the action space. We fit an additively separable...

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Bibliographic Details
Published in:	Operations research Vol. 56; no. 3; pp. 712 - 727
Main Authors:	Adelman, Daniel, Mersereau, Adam J
Format:	Journal Article
Language:	English
Published:	Linthicum, MD INFORMS 01.05.2008 Institute for Operations Research and the Management Sciences
Subjects:	Algorithms Analysis Applied sciences approximate dynamic programming Approximation column generation discounted infinite horizon Dynamic programming dynamic programming/optimal control Exact sciences and technology Integer programming Integers Lagrange multiplier Lagrangian function Lagrangian optimization Learning models (Stochastic processes) Linear programming Mathematical functions Mathematical programming Numberings Operational research and scientific management Operational research. Management science Optimal solutions Optimization algorithms Problem sets Problem solving Stochastic models Studies United States dynamic programming/optimal control: approximate dynamic programming Lagrangian Discount Lagrangian optimization Tolerance Linear programming Function approximation Optimization Stochastic programming Separation method Column generation Lagrange multiplier Optimal control discounted infinite horizon; linear programming: column generation Relaxation method Infinite horizon Dynamic programming
ISSN:	0030-364X, 1526-5463
Online Access:	Get full text
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