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 Sniedovich, Moshe, 1945
 2nd ed.  Boca Raton, FL : CRC Press, c2011.
 Description
 Book — xiii, 604 p. : ill. ; 25 cm.
 Summary

 Introduction Welcome to Dynamic Programming! How to Read This Book SCIENCE Fundamentals Introduction MetaRecipe Revisited Problem Formulation Decomposition of the Solution Set Principle of Conditional Optimization Conditional Problems Optimality Equation Solution Procedure Time Out: Direct Enumeration! Equivalent Conditional Problems Modified Problems The Role of a Decomposition Scheme Dynamic Programming Problem  Revisited Trivial Decomposition Scheme Summary and a Look Ahead Multistage Decision Model Introduction A Prototype Multistage Decision Model Problem vs Problem Formulation Policies Markovian Policies Remarks on the Notation Summary Bibliographic Notes Dynamic Programming  An Outline Introduction Preliminary Analysis Markovian Decomposition Scheme Optimality Equation Dynamic Programming Problems The Final State Model Principle of Optimality Summary Solution Methods Introduction Additive Functional Equations Truncated Functional Equations Nontruncated Functional Equations Summary Successive Approximation Methods Introduction Motivation Preliminaries Functional Equations of Type One Functional Equations of Type Two Truncation Method Stationary Models Truncation and Successive Approximation Summary Bibliographic Notes Optimal Policies Introduction Preliminary Analysis Truncated Functional Equations Nontruncated Functional Equations Successive Approximation in the Policy Space Summary Bibliographic Notes The Curse of Dimensionality Introduction Motivation Discrete Problems Special Cases Complete Enumeration Conclusions The Rest Is Mathematics and Experience Introduction Choice of Model Dynamic Programming Models Forward Decomposition Models Practice What You Preach! Computational Schemes Applications Dynamic Programming Software Summary ART Refinements Introduction WeakMarkovian Condition Markovian Formulations Decomposition Schemes Sequential Decision Models Example Shortest Path Model The Art of Dynamic Programming Modeling Summary Bibliographic Notes The State Introduction Preliminary Analysis Mathematically Speaking Decomposition Revisited Infeasible States and Decisions State Aggregation Nodes as States Multistage vs Sequential Models Models vs Functional Equations Easy Problems Modeling Tips Concluding Remarks Summary Parametric Schemes Introduction Background and Motivation Fractional Programming Scheme CProgramming Scheme Lagrange Multiplier Scheme Summary Bibliographic Notes The Principle of Optimality Introduction Bellman's Principle of Optimality Prevailing Interpretation Variations on a Theme Criticism So What Is Amiss? The Final State Model Revisited Bellman's Treatment of Dynamic Programming Summary Post Script: Pontryagin's Maximum Principle Forward Decomposition Introduction Function Decomposition Initial Problem Separable Objective Functions Revisited Modified Problems Revisited Backward Conditional Problems Revisited Markovian Condition Revisited Forward Functional Equation Impact on the State Space Anomaly Pathologic Cases Summary and Conclusions Bibliographic Notes Push! Introduction The Pull Method The Push Method Monotone Accumulated Return Processes Dijkstra's Algorithm Summary Bibliographic Notes EPILOGUE What Then Is Dynamic Programming? Review NonOptimization Problems An Abstract Dynamic Programming Model Examples The Towers of Hanoi Problem OptimizationFree Dynamic Programming Concluding Remarks Appendix A: Contraction Mapping Appendix B: Fractional Programming Appendix C: Composite Concave Programming Appendix D: The Principle of Optimality in Stochastic Processes Appendix E: The Corridor Method Bibliography Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
T57.83 .S65 2011  Unknown 
2. Dynamic programming [1992]
 Sniedovich, Moshe, 1945
 New York, N.Y. : M. Dekker, c1992.
 Description
 Book — viii, 410 p. : ill. : 24 cm.
 Summary

This title portrays dynamic programming as a methodology, identifying its constituent components, and explaining how it approaches problems and tackles them. It does not consider it as a practical tool, nor how it might address any actual situations in the real world. It assumes calculus, and set theory.
(source: Nielsen Book Data)
 Online
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request 
T57.83 .S65 1992  Available 
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