Mathematical indection, and its use in solving optimizaiton problems, is a topic of great interest with many applications. It enables us to study multistage decision problems by proceeding backwards in time, using a method called dynamic programming. All the techniques needed to solve the various problems are explained, and the author's fluent style will leave the reader with an avid interest in the subject.
- Tailored to the needs of students of optmization and decision theory
- Written in a lucid style with numerous examples and applications
- Coverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexity
- Coverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topics
- Coverage of advanced topics: Markov decision processes, minimizing expected costs, policy improvements and problems with unknown statistical parameters
- Contains exercises at the end of each chapter, with hints in an appendix
Aimed primrily at students of mathematics and statistics, the lucid text will also appeal to engineering and students and those working in the areas of optimization and operations research.
1. Introduction
2. Multi-Stage Decision Problems
3. Networks
4. Further Applications
5. Convexity
6. Markov Systems
7. Optimal Stopping
8. Special Problems
9. General Theory
10. Minimizing Average Costs
etc.