Information Relaxations and Duality in Stochastic Dynamic Programs: A Review and Tutorial

Dynamic Programming (DP) is a powerful framework for modeling complex decision problems, but it is difficult to scale to complex problems. Information relaxation methods reduce a complex stochastic DP to a series of scenario-specific deterministic optimization problems solved within a Monte Carlo simulation, providing a "one-stop-shop" for researchers seeking to learn the key ideas and tools for using information relaxation methods.

Format: Paperback / softback
Length: 108 pages
Publication date: 21 March 2022
Publisher: now publishers Inc

Dynamic Programming (DP) is a powerful framework for modeling complex decision problems where uncertainty is resolved and decisions are made over time. However, scaling DP to complex problems can be challenging. Monte Carlo simulation methods, while scalable, often lack a good way to identify an optimal policy or provide a performance bound. To address these restrictions, the authors review the information relaxation approach, which reduces a complex stochastic DP to a series of scenario-specific deterministic optimization problems solved within a Monte Carlo simulation. Written in a tutorial style, the authors summarize the key ideas of information relaxation methods for stochastic DPs and demonstrate their use in several examples. They provide a "one-stop-shop" for researchers seeking to learn the key ideas and tools for using information relaxation methods.

This book offers a comprehensive overview of a powerful technique for use by students, researchers, and practitioners. It covers the theoretical foundations, algorithms, and applications of information relaxation methods in stochastic DP, providing a comprehensive guide for those seeking to leverage this technique in their work.

Weight: 165g
Dimension: 234 x 156 (mm)
ISBN-13: 9781680839623