- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
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.
Contents
1. Introduction
2. Basic Framework
3. Main Results
4. Convex Dynamic Programs
5. Summary of the Information Relaxation Approach
6. Example: Inventory Management
7. Example: Dynamic Assortment Planning
8. Example: Portfolio Optimization with Transaction Costs
9. Advances in Methodology
10. Applications
11. Conclusions
References
