Dynamic Programming (DP) provides a powerful framework for modeling complex decision problems where uncertainty is resolved and decisions are made over time. But it is difficult to scale to complex problems. Monte Carlo simulation methods, however, typically scale well, but typically do not provide 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 works by reducing 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 provides the reader with a comprehensive overview of a powerful technique for use by students, researchers and practitioners.
- ISBN13 9781680839623
- Publish Date 21 March 2022
- Publish Status Active
- Publish Country US
- Imprint now publishers Inc
- Format Paperback
- Pages 108
- Language English
- URL https://nowpublishers.com/article/Details/OPT-027