Dynamic programming and optimal control /

Dynamic programming and optimal control /

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

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