Dynamic Programming and Its Applications
Proceedings of the International Conference on Dynamic Programming and Its Applications, University of British Columbia, Vancouver, British Columbia, Canada, April 14-16, 1977
Published on 10. May 2014
426 pages
978-1-4832-5894-2 (ISBN)
Description
Dynamic Programming and Its Applications provides information pertinent to the theory and application of dynamic programming. This book presents the development and future directions for dynamic programming. Organized into four parts encompassing 23 chapters, this book begins with an overview of recurrence conditions for countable state Markov decision problems, which ensure that the optimal average reward exists and satisfies the functional equation of dynamic programming. This text then provides an extensive analysis of the theory of successive approximation for Markov decision problems. Other chapters consider the computational methods for deterministic, finite horizon problems, and present a unified and insightful presentation of several foundational questions. This book discusses as well the relationship between policy iteration and Newton's method. The final chapter deals with the main factors severely limiting the application of dynamic programming in practice. This book is a valuable resource for growth theorists, economists, biologists, mathematicians, and applied management scientists.
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M.L. Puterman
Dynamic Programming and Its Applications
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01/1978
Academic Press
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Content
ContributorsPrefaceSurveys Recurrence Conditions in Denumerable State Markov Decision Processes Discounted and Undiscounted Value-Iteration in Markov Decision Problems: A Survey Computational Advances in Dynamic Programming The Analytic Theory of Policy Iteration Dynamic Programming in Borei Spaces Elimination of Nonoptimal Actions in Markov Decision ProcessesApplications On Renewal Decisions Steady-State Policies, Dynamic Programming, and Optimal Economic Growth Comments on the Origin and Application of Markov Decision Processes The Application of Markov Decision Processes to Forest Management An Application of Dynamic Programming in Statistics Some Dynamic Programming Applications in Fisheries ManagementTheory Buckets, Shortest Paths, and Integer Programming Affine Dynamic Programming Optimal Control of a Diffusion Process with Reflecting Boundaries and both Continous and Lump Costs On Approximate Solutions of Finite-Stage Dynamic Programs An Inverse Theorem between Main and Inverse Dynamic Programming: Infinite-Stage Case On the Transient Case for Markov Decision Chains with General State Spaces An Operator-Theoretical Treatment of Negative Dynamic Programming Existence of Average Optimal Strategies in Markovian Decision Problems with Strictly Unbounded CostsInternational Conference on Dynamic Programming: Panel Discussion Comments Of Karl Hinderer Comments Of Eric V. Denardo Comments Of Arthur F. Veinott, Jr.Participants
