β Scribed by Karl Hinderer, Ulrich Rieder, Michael Stieglitz (auth.)
No coin nor oath required. For personal study only.
This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance.
Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Front Matter....Pages i-xxii
Introduction and Organization of the Book....Pages 1-11
Front Matter....Pages 13-13
The Stationary Deterministic Model and the Basic Solution Procedure....Pages 15-33
Additional General Issues....Pages 35-49
Examples of Deterministic Dynamic Programs....Pages 51-68
Absorbing Dynamic Programs and Acyclic Networks....Pages 69-86
Monotonicity of the Value Functions....Pages 87-104
Concavity and Convexity of the Value Functions....Pages 105-123
Monotone and ILIP Maximizers....Pages 125-148
Existence of Optimal Action Sequences....Pages 149-167
Stationary Models with Large Horizon....Pages 169-186
Front Matter....Pages 187-187
Control Models with Disturbances....Pages 189-198
Markovian Decision Processes with Finite Transition Law....Pages 199-219
Examples of Markovian Decision Processes with Finite Transition Law....Pages 221-257
Markovian Decision Processes with Discrete Transition Law....Pages 259-275
Examples with Discrete Disturbances and with Discrete Transition Law....Pages 277-289
Models with Arbitrary Transition Law....Pages 291-318
Existence of Optimal Policies....Pages 319-325
Stochastic Monotonicity and Monotonicity of the Value Functions....Pages 327-339
Concavity and Convexity of the Value Functions and Monotone Maximizers....Pages 341-346
Markovian Decision Processes with Large and with Infinite Horizon....Pages 347-352
Front Matter....Pages 353-353
Markovian Decision Processes with Disturbances....Pages 355-370
Markov Renewal Programs....Pages 371-387
Bayesian Control Models....Pages 389-410
Examples of Bayesian Control Models....Pages 411-434
Bayesian Models with Disturbances....Pages 435-454
Partially Observable Models....Pages 455-479
Back Matter....Pages 481-527
Operations Research, Management Science;Systems Theory, Control;Discrete Optimization;Probability Theory and Stochastic Processes
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<p>Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey c