not what i expected | Dynamic Programming & Optimal Control, Vol. I

book: Dynamic Programming & Optimal Control, Vol. I | Dimitri P. Bertsekas

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	• Dynamic Programming & Optimal Control, Vol. I Dimitri P. Bertsekas Athena Scientific, 2005 - 558 pages average customer review:based on 2 reviews view larger image
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Best book I've used so far.

This book does a very good job presenting both deterministic and stochastic optimal control. The author does a particularly good job in presenting the derivation of the Bellman equation and its relation to variational formulations for deterministic optimal control. There are also many very good problems with which the reader can test her understanding, and the author has made many solutions available on his web page. Problems range from testing theoretical understanding to determinig optimal policies for various control problems. There are even some exercises which ask the reader to develop parallel codes to solve some problems, so I think there is something in this book for everybody. Richard Bellman once said that there is considerably more to optimal control than just locating the eigenvalues of some matrix in the complex plane. I believe that Bertsekas has remained faithful to Bellman's view with the broad range of problems which he attacks through dynamic programming. I am currently doing a PhD thesis in mathematics studying Bellman equations, and I cannot think of a better source for intuition about control problems than Bertsekas' book. He even does a nice job in pointing out where he has omitted technicalities in the mathematical treatment for those who wish a very rigorous approach to control. If there is a better book out there, I am not aware of it.

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not what i expected

This book has so many unnecessary material in it. This makes you tired if you want to read it as your course book. I think Bertsekas, is more busy to write as many as book he can instead of making them readable.

The first of the two volumes of the leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an introduction to the far-reaching methodology of Neuro-Dynamic Programming. The first volume is oriented towards modeling, conceptualization, and finite-horizon problems, but also includes a substantive introduction to infinite horizon problems that is suitable for classroom use. The second volume is oriented towards mathematical analysis and computation, and treats infinite horizon problems extensively. The text contains many illustrations, worked-out examples, and exercises.

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