∗ Tailored to the needs of students of optimization 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 primarily at students of mathematics and statistics, the lucid text will also appeal to engineering and science students and those working in the areas of optimization and operations research.
Multi–Stage Decision Problems.
MARKOV DECISION PROCESSES.
Minimizing Average Costs.
Notes on the Exercises.