Simulation-Based Optimization
Parametric Optimization Techniques and Reinforcement Learning
(Sprache: Englisch)
PSTRONGSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning/STRONG introduces the evolving area of simulation-based optimization. /P PThe book's objective is two-fold: (1) It examines the mathematical governing...
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PSTRONGSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning/STRONG introduces the evolving area of simulation-based optimization. /P
PThe book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. BRBroadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: BR*An accessible introduction to reinforcement learning and parametric-optimization techniques. BR*A step-by-step description of several algorithms of simulation-based optimization. BR*A clear and simple introduction to the methodology of neural networks. BR*A gentle introduction to convergence analysis of some of the methods enumerated above. BR*Computer programs for many algorithms of simulation-based optimization. /P
Klappentext zu „Simulation-Based Optimization “
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: An accessible introduction to reinforcement learning and parametric-optimization techniques. A step-by-step description of several algorithms of simulation-based optimization. A clear and simple introduction to the methodology of neural networks. A gentle introduction to convergence analysis of some of the methods enumerated above. Computer programs for many algorithms of simulation-based optimization.
Inhaltsverzeichnis zu „Simulation-Based Optimization “
List of Figures. List of Tables. Acknowledgements. Preface1. Background
1.1. Why this book was written
1.2. Simulation-based optimization and modern times
1.3. How this book is organized
2. Notation
2.1. Chapter Overview
2.2. Some basic conventions
2.3. Vector notation
2.4. Notation for matrices
2.5. Notation for n-tuples
2.6. Notation for sets
2.7. Notation for sequences
2.8. Notation for transformations
2.9. Max, min and arg max
2.10. Acronyms and abbreviations
3. Probability theory: a refresher.3.1. Overview of this chapter
3.2. Laws of probability
3.3. Probability distributions
3.4. Expected value of a random variable
3.5. Standard deviation of a random variable
3.6. Limit theorems
3.7. Review questions
4. Basic concepts underlying simulation
4.1. Chapter overview
4.2. Introductions
4.3. Models
4.4. Simulation modeling of random systems
4.5. Concluding remarks
4.6. Historical remarks
4.7. Review questions
5. Simulation optimization: an overview
5.1. Chapter overview
5.2. Stochastic parametric optimization
5.3. Stochastic control optimization
5.4. Historical remarks
5.5. Review questions
6. Response surfaces and neural nets
6.1. Chapter overview
6.2. RSM: an overview
6.3. RSM: details
6.4. Neuro-response surface methods
6.5. Concluding remarks
6.6. Bibliographic remarks
6.7. Review questions
7. Parametric optimization
7.1. Chapter overview
7.2. Continuous optimization
7.3. Discrete optimization
7.4. Hybrid solution spaces
7.5. Concluding remarks
7.6. Bibliographic remarks
7.7. Review questions
8. Dynamic programming
8.1. Chapter overview
8.2. Stochastic processes
8.3. Markov processes, Markov chains and semi-Markov processes
8.4. Markov decision problems
8.5. How to solve an MDP using exhaustive enumeration
8.6. Dynamic programming for average reward
8.7. Dynamic programming and discounted reward
8.8. The Bellman equation: an intuitive perspective
8.9. Semi-Markov decision problems
8.10. Modified
... mehr
policy iteration
8.11. Miscellaneous topics related to MDPs and SMDPs
8.12. Conclusions
8.13. Bibliographic remarks
8.14. Review questions
9. Reinforcement learning
9.1. Chapter overview
9.2. The need for reinforcement learning
9.3. Generating the TPM through straightforward counting
9.4. Reinforcement learning: fundamentals
9.5. Discounted reward reinforcement learning
9.6. Average reward reinforcement learning
9.7. Semi-Markov decision problems and RL
9.8. RL algorithms and their DP counterparts
9.9. Actor-critic algorithms
9.10. Model-building algorithms
9.11. Finite horizon problems
9.12. Function approximation
9.13. Conclusions
9.14. Bibliographic remarks
9.15. Review questions
10. Markov chain automata theory
10.1. Chapter overview
10.2. The MCAT framework
10.3. Concluding remarks
10.4. Bibliographic remarks
10.5. Review questions
11. Convergence: background material
11.1. Chapter overview
11.3. Norms
11.4. Normed vector spaces
11.5. Functions and mappings
11.6. Mathematical induction
11.7. Sequences
11.8. Sequences in n
11.9. Cauchy sequences in n
11.10. Contraction mappings in n
11.11. Bibliographic remarks
11.12. Review questions
12. Convergence: parametric optimization
12.1. Chapter overview
12.2. Some definitions and a result
12.3. Convergence of gradient-descent approaches
12.4. Perturbation estimates
12.5. Convergence of simulated annealing
12.6. Concluding remarks
12.7. Bibliographic remarks
12.8. Review questions
13. Convergence: control optimization
13.1. Chapter overview
13.2. Dynamic programming transformations
13.3. Some definitions
13.4. Monotonicity of T, T&mgr;, L, and L&mgr;
13.5. Some results for average and discounted MDPs
13.6. Discounted reward and classical dynamic programming
13.7. Average reward and classical dynamic programming
13.8. Convergence of DP schemes for SMDPs
13.9. Convergence of reinforcement learning schemes
13.10. Background material for RL convergence
13.11. Key results for RL convergence
13.12. Convergence of RL based on value iteration
13.13. Convergence of Q learning
13.14. SDMPs
13.15. Convergence of actor critic algorithms
13.16. Function approximation and convergence analysis
13.17. Bibliographic remarks
13.18. Review questions
14. Case studies
14.1. Chapter overview
14.2. A classical inventory control problem
14.3. Airline yield management
14.4. Preventive maintenance
14.5. Transfer line buffer optimization
14.6. Inventory control in a supply chain
14.7. AGV routing
14.8. Quality control
14.9. Elevator scheduling
14.10. Simulation optimization: a comparative perspective
14.11. Concluding remarks
14.12. Review questions
15. Codes
15.1. Introduction
15.2. C programming
15.3. Code organization
15.4. Random number generators
15.5. Simultaneous perturbation
15.6. Dynamic programming codes
15.7. Codes for neural networks
15.8. Reinforcement learning codes
15.9. Codes for the preventative maintenance case study
15.10. MATLAB codes
15.11. Concluding remarks
15.12. Review questions
16. Concluding remarks
References
Indexions
14. Case studies
14.1. Chapter overview
14.2.
8.11. Miscellaneous topics related to MDPs and SMDPs
8.12. Conclusions
8.13. Bibliographic remarks
8.14. Review questions
9. Reinforcement learning
9.1. Chapter overview
9.2. The need for reinforcement learning
9.3. Generating the TPM through straightforward counting
9.4. Reinforcement learning: fundamentals
9.5. Discounted reward reinforcement learning
9.6. Average reward reinforcement learning
9.7. Semi-Markov decision problems and RL
9.8. RL algorithms and their DP counterparts
9.9. Actor-critic algorithms
9.10. Model-building algorithms
9.11. Finite horizon problems
9.12. Function approximation
9.13. Conclusions
9.14. Bibliographic remarks
9.15. Review questions
10. Markov chain automata theory
10.1. Chapter overview
10.2. The MCAT framework
10.3. Concluding remarks
10.4. Bibliographic remarks
10.5. Review questions
11. Convergence: background material
11.1. Chapter overview
11.3. Norms
11.4. Normed vector spaces
11.5. Functions and mappings
11.6. Mathematical induction
11.7. Sequences
11.8. Sequences in n
11.9. Cauchy sequences in n
11.10. Contraction mappings in n
11.11. Bibliographic remarks
11.12. Review questions
12. Convergence: parametric optimization
12.1. Chapter overview
12.2. Some definitions and a result
12.3. Convergence of gradient-descent approaches
12.4. Perturbation estimates
12.5. Convergence of simulated annealing
12.6. Concluding remarks
12.7. Bibliographic remarks
12.8. Review questions
13. Convergence: control optimization
13.1. Chapter overview
13.2. Dynamic programming transformations
13.3. Some definitions
13.4. Monotonicity of T, T&mgr;, L, and L&mgr;
13.5. Some results for average and discounted MDPs
13.6. Discounted reward and classical dynamic programming
13.7. Average reward and classical dynamic programming
13.8. Convergence of DP schemes for SMDPs
13.9. Convergence of reinforcement learning schemes
13.10. Background material for RL convergence
13.11. Key results for RL convergence
13.12. Convergence of RL based on value iteration
13.13. Convergence of Q learning
13.14. SDMPs
13.15. Convergence of actor critic algorithms
13.16. Function approximation and convergence analysis
13.17. Bibliographic remarks
13.18. Review questions
14. Case studies
14.1. Chapter overview
14.2. A classical inventory control problem
14.3. Airline yield management
14.4. Preventive maintenance
14.5. Transfer line buffer optimization
14.6. Inventory control in a supply chain
14.7. AGV routing
14.8. Quality control
14.9. Elevator scheduling
14.10. Simulation optimization: a comparative perspective
14.11. Concluding remarks
14.12. Review questions
15. Codes
15.1. Introduction
15.2. C programming
15.3. Code organization
15.4. Random number generators
15.5. Simultaneous perturbation
15.6. Dynamic programming codes
15.7. Codes for neural networks
15.8. Reinforcement learning codes
15.9. Codes for the preventative maintenance case study
15.10. MATLAB codes
15.11. Concluding remarks
15.12. Review questions
16. Concluding remarks
References
Indexions
14. Case studies
14.1. Chapter overview
14.2.
... weniger
Bibliographische Angaben
- Autor: Abhijit Gosavi
- 2003, 554 Seiten, Maße: 15,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 1402074549
- ISBN-13: 9781402074547
- Erscheinungsdatum: 30.06.2003
Sprache:
Englisch
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