Dynamic Optimization

Beschreibung

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.
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.

Provides a self-contained and easy-to-read introduction to dynamic programming Provides a comprehensive treatment of discrete-time multistage optimization Presents the theory of Markov decision processes without advanced measure theory Includes various examples and exercises (without solutions) Includes supplementary material: sn.pub/extras

Autor*in

Karl Hinderer

Themen in »Dynamic Optimization«

dynamic programming Markov decision processes discrete-time multi-stage optimization networks stochastic optimal control Markov renewal programs Bayesian control models partially observable processes

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“Part I deals with deterministic dynamic optimization models describing the control of discrete-time systems. Part II is devoted to discrete-time stochastic control models. Part III … is devoted to Markovian decision processes with disturbances… The book comprises a lot of examples, problems for readers, and supplements with additional comments for the advanced reader and with bibliographic notes.” (Svetlana A. Kravchenko, zbMATH 1365.90002)
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