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|a Bertsekas, Dimitri P
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|a Bertsekas, Dimitri P
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|a Dynamic programming and optimal control /
|c Dimitri P. Bertsekas
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|a Fourth edition
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|a Belmont, Mass. :
|b Athena Scientific,
|c ©2012-2017
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|a Belmont, Mass. :
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|a Volume 1 published in 2017
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|a Includes bibliographical references and indexes
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|a v. 1. [no special title] -- v. 2. Approximate dynamic programming
|
505 |
0 |
0 |
|g VOLUME 1 :
|g 1
|t THE DYNAMIC PROGRAMMING ALGORITHM --
|g 1.1.
|t Introduction, p.2 --
|g 1.2.
|t The basic problem, p.14 --
|g 1.3.
|t The dynamic programming algorithm, p.20 --
|g 1.4.
|t State augmentation and other reformulations, p.37 --
|g 1.5.
|t Some mathematical issues, p.44 --
|g 1.6.
|t Dynamic programming and minimax control, p.49 --
|g 1.7.
|t Notes, sources, excercises, p.53 --
|g 2.
|t DETERMINISTIC SYSTEMS AND THE SHORTEST PATH PROBLEM --
|g 2.1.
|t Finite-state systems and shortest paths, p.69 --
|g 2.2.
|t Some shortest path applications, p.72 --
|g 2.3.
|t Shortest path algorithms, p.81 --
|g 2.4.
|t Notes, sources, and exercises, p.101 --
|g 3.
|t PROBLEMS WITH PERFECT STATE INFORMATION --
|g 3.1.
|t Linear systems and quadratic cost, p.110 --
|g 3.2.
|t Inventory control, p.125 --
|g 3.3.
|t Dynamic portfolio analysis, p.134 --
|g 3.4.
|t Optimal stopping problems, p.140 --
|g 3.5.
|t Scheduling an dthe interchange argument, p.150 --
|g 3.6.
|t Set-membership description of uncertainty, p.154 --
|g 3.7.
|t Notes, sources, exercises, p.165 --
|g 4.
|t PROBLEMS WITH IMPERFECT STATE INFORMATION --
|g 4.1.
|t Reduction to the perfect information case, p.184 --
|g 4.2.
|t Linear systems and quadratic cost, p.195 --
|g 4.3.
|t Sufficient statistics, p.202 --
|g 4.4.
|t Notes, sources, and exercises, p.221 --
|g 5.
|t INTRODUCTION TO INFINITE HORIZON PROBLEMS --
|g 5.1.
|t An overwiew, p.232 --
|g 5.2.
|t Stochastic shortest path problems, p.236 --
|g 5.3.
|t Computational methods, p.245 --
|g 5.4.
|t Discounted problems, p.249 --
|g 5.5.
|t Average cost per stage problems, p.253 --
|g 5.6.
|t Semi-Markov problems, p.267 --
|g 5.7.
|t Notes, sources, and exercises, p.277 --
|g 6.
|t APPROXIMATE DYNAMIC PROGRAMMING --
|g 6.1.
|t Cost approximation and limited lookahead, p.296 --
|g 6.2.
|t Problem approximation, p.307 --
|g 6.3.
|t Parametric cost approximation, p.327 --
|g 6.4.
|t On-line approximation and optimization, p.352 --
|g 6.5.
|t Simulation-based cost-to-go approximation, p.389 --
|g 6.6.
|t Aproximation in policy space, p.395 --
|g 6.7.
|t Adaptive control, p.397 --
|g 6.8.
|t Discretization issues, p.405 --
|g 6.9.
|t Notes, sources, and exercises, p.408 --
|g 7.
|t DETERMINISTIC CONTINUOUS-TIME OPTIMAL CONTROL --
|g 7.1.
|t Continuous-time optimal control, p.426 --
|g 7.2.
|t The Hamilton-Jakobi-Bellman equation, p.429 --
|g 7.3.
|t The Pontryagin minimum principle, p.435 --
|g 7.4.
|t Extensions of the minimum principle, p.451 --
|g 7.5.
|t Notes, sources, and exercises, p.461 --
|g Appendix A:
|t A MATHEMATICAL REVIEW --
|g Appendix B:
|t ON OPTIMIZATION THEORY --
|g Appendix C:
|t ON PROBABILITY THEORY --
|g Appendix D:
|t ON FINITE-STATE MARKOV CHAINS --
|g Appendix E:
|t LEAST SQUARES ESTIMATION AND KALMAN FILTERING --
|g Appendix F:
|t FORMULATING PROBLEMS OF DECISION UNDER UNCERTAINTY -- References, p.533 -- Index, p.551
|
505 |
0 |
0 |
|g VOLUME 1 :
|g 1
|t THE DYNAMIC PROGRAMMING ALGORITHM --
|g 1.1.
|t Introduction, p.2 --
|g 1.2.
|t The basic problem, p.14 --
|g 1.3.
|t The dynamic programming algorithm, p.20 --
|g 1.4.
|t State augmentation and other reformulations, p.37 --
|g 1.5.
|t Some mathematical issues, p.44 --
|g 1.6.
|t Dynamic programming and minimax control, p.49 --
|g 1.7.
|t Notes, sources, exercises, p.53 --
|g 2.
|t DETERMINISTIC SYSTEMS AND THE SHORTEST PATH PROBLEM --
|g 2.1.
|t Finite-state systems and shortest paths, p.69 --
|g 2.2.
|t Some shortest path applications, p.72 --
|g 2.3.
|t Shortest path algorithms, p.81 --
|g 2.4.
|t Notes, sources, and exercises, p.101 --
|g 3.
|t PROBLEMS WITH PERFECT STATE INFORMATION --
|g 3.1.
|t Linear systems and quadratic cost, p.110 --
|g 3.2.
|t Inventory control, p.125 --
|g 3.3.
|t Dynamic portfolio analysis, p.134 --
|g 3.4.
|t Optimal stopping problems, p.140 --
|g 3.5.
|t Scheduling and the interchange argument, p.150 --
|g 3.6.
|t Set-membership description of uncertainty, p.154 --
|g 3.7.
|t Notes, sources, exercises, p.165 --
|g 4.
|t PROBLEMS WITH IMPERFECT STATE INFORMATION --
|g 4.1.
|t Reduction to the perfect information case, p.184 --
|g 4.2.
|t Linear systems and quadratic cost, p.195 --
|g 4.3.
|t Sufficient statistics, p.202 --
|g 4.4.
|t Notes, sources, and exercises, p.221 --
|g 5.
|t INTRODUCTION TO INFINITE HORIZON PROBLEMS --
|g 5.1.
|t An overview, p.232 --
|g 5.2.
|t Stochastic shortest path problems, p.236 --
|g 5.3.
|t Computational methods, p.245 --
|g 5.4.
|t Discounted problems, p.249 --
|g 5.5.
|t Average cost per stage problems, p.253 --
|g 5.6.
|t Semi-Markov problems, p.267 --
|g 5.7.
|t Notes, sources, and exercises, p.277 --
|g 6.
|t APPROXIMATE DYNAMIC PROGRAMMING --
|g 6.1.
|t Cost approximation and limited lookahead, p.296 --
|g 6.2.
|t Problem approximation, p.307 --
|g 6.3.
|t Parametric cost approximation, p.327 --
|g 6.4.
|t On-line approximation and optimization, p.352 --
|g 6.5.
|t Simulation-based cost-to-go approximation, p.389 --
|g 6.6.
|t Aproximation in policy space, p.395 --
|g 6.7.
|t Adaptive control, p.397 --
|g 6.8.
|t Discretization issues, p.405 --
|g 6.9.
|t Notes, sources, and exercises, p.408 --
|g 7.
|t DETERMINISTIC CONTINUOUS-TIME OPTIMAL CONTROL --
|g 7.1.
|t Continuous-time optimal control, p.426 --
|g 7.2.
|t The Hamilton-Jakobi-Bellman equation, p.429 --
|g 7.3.
|t The Pontryagin minimum principle, p.435 --
|g 7.4.
|t Extensions of the minimum principle, p.451 --
|g 7.5.
|t Notes, sources, and exercises, p.461 --
|g Appendix A:
|t A MATHEMATICAL REVIEW --
|g Appendix B:
|t ON OPTIMIZATION THEORY --
|g Appendix C:
|t ON PROBABILITY THEORY --
|g Appendix D:
|t ON FINITE-STATE MARKOV CHAINS --
|g Appendix E:
|t LEAST SQUARES ESTIMATION AND KALMAN FILTERING --
|g Appendix F:
|t FORMULATING PROBLEMS OF DECISION UNDER UNCERTAINTY -- References, p.533 -- Index, p.551
|
505 |
0 |
0 |
|g VOLUME 2 :
|t Approximate Dynamic Programming --
|g 1
|t DICOUNTED PROBLEMS -- THEORY --
|g 1.1.
|t Minimization of total cost -- introduction, p.3 --
|g 1.2.
|t Discounted problems -- bounded cost per stage, p.14 --
|g 1.3.
|t Scheduling and multiarmed bandit problems, p.22 --
|g 1.4.
|t Discounted continuous-time problems, p.32 --
|g 1.5.
|t The role of contraction mappings, p.45 --
|g 1.6.
|t General forms of discounted dynamic programming, p.57 --
|g 1.7.
|t Notes, sources, and exercises, p.71 --
|g 2.
|t DISCOUNTED PROBLEMS -- COMPUTATIONAL METHODS --
|g 2.1.
|t Markovian decision problems, p.82 --
|g 2.2.
|t Value iteration, p.84 --
|g 2.3.
|t Policy iteration, p.97 --
|g 2.4.
|t Linear programming methods, p.112 --
|g 2.5.
|t Methods for general discounted problems, p.115 --
|g 2.6.
|t Asynchronous algorithms, p.138 --
|g 2.7.
|t Notes, Sources, and exercises, p.156 --
|g 3.
|t STOCHASTIC SHORTEST PATH PROBLEMS --
|g 3.1.
|t Problem formulation, p.172 --
|g 3.2.
|t Main results, p.175 --
|g 3.3.
|t Underlying contraction properties, p.182 --
|g 3.4.
|t Value iteration, p.184 --
|g 3.5.
|t Policy iteration, p.189 --
|g 3.6.
|t Countable-state problems, p.201 --
|g 3.7.
|t Notes, sources,and exercises, p.204 --
|g 4.
|t UNDISCOUNTED PROBLEMS --
|g 4.1.
|t Unbounded costs per stage, p.214 --
|g 4.2.
|t Linear systems and quadratic, p.231 --
|g 4.3.
|t Inventory control, p.233 --
|g 4.4.
|t Optimal stopping, p.235 --
|g 4.5.
|t Optimal gambling strategies, p.241 --
|g 4.6.
|t Continuous-time problems -- control of queues, p.248 --
|g 4.7.
|t Nonstationary and periodic problems, p.256 --
|g 4.8.
|t Notes, sources, and exercises, p.261 --
|g 5.
|t AVERAGE COST PER STAGE PROBLEMS --
|g 5.1.
|t Finite-spaces average cost models, p.274 --
|g 5.2.
|t Conditions for equal average cost for all initial states, p.298 --
|g 5.3.
|t Value iteration, p.304 --
|g 5.4.
|t Policy iteration, p.329 --
|g 5.5.
|t Linear programming, p.339 --
|g 5.6.
|t Infinite-spaces average cost models, p.345 --
|g 5.7.
|t Notes, sources, and exercises, p.374 --
|g 6.
|t APPROXIMATE DYNAMIC PROGRAMMING -- DISCOUNTED MODELS --
|g 6.1.
|t General issues of simulation-based cost approximation, p.391 --
|g 6.2.
|t Direct policy evaluation -- gradient methods, p.418 --
|g 6.3.
|t Projected Equation methods for policy evaluation, p.423 --
|g 6.4.
|t Policy iteration issues, p.451 --
|g 6.5.
|t Aggregation methods, p.474 --
|g 6.6
|t Q-learning, p.493 --
|g 6.7.
|t Notes, sources, and exercises, p.511 --
|g 7.
|t APPROXIMATE DYNAMIC PROGRAMMING -- NONDISCOUNTED MODELS AND GENERALIZATIONS --
|g 7.1.
|t Stochastic shortest path problems, p.532 --
|g 7.2.
|t Average cost problems, p.537 --
|g 7.3.
|t General problems and Monte Carlo linear algebra, p.552 --
|g 7.4.
|t Approximation in policy space, p.620 --
|g 7.5.
|t Notes, sources, and exercises, p.629 --
|g Appendix A :
|t MEASURE-THEORETIC ISSUES IN DYNAMIC PROGRAMMING -- References, p.657 -- Index, p.691
|
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|a Volume 1. [no special title] -- volume 2. Approximate dynamic programming
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|a v. 1. [no special title] -- v. 2. Approximate dynamic programming
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