Ebook: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Author: Abhijit Gosavi (auth.)
- Tags: Systems Theory Control, Calculus of Variations and Optimal Control, Optimization, Operation Research/Decision Theory, Optimization
- Series: Operations Research/Computer Science Interfaces Series 25
- Year: 2003
- Publisher: Springer US
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages i-xxvii
Background....Pages 1-8
Notation....Pages 9-13
Probability Theory: A Refresher....Pages 15-28
Basic Concepts Underlying Simulation....Pages 29-45
Simulation-Based Optimization: An Overview....Pages 47-55
Parametric Optimization: Response Surfaces Neural Networks....Pages 57-91
Parametric Optimization: Simultaneous Perturbation & Meta-Heuristics....Pages 93-132
Control Optimization with Stochastic Dynamic Programming....Pages 133-210
Control Optimization with Reinforcement Learning....Pages 211-275
Control Optimization with Learning Automata....Pages 277-285
Convergence: Background Material....Pages 287-315
Convergence Analysis of Parametric Optimization Methods....Pages 317-342
Convergence Analysis of Control Optimization Methods....Pages 343-408
Case Studies....Pages 409-431
Codes....Pages 433-535
Concluding Remarks....Pages 537-538
Back Matter....Pages 539-554
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.
Content:
Front Matter....Pages i-xxvii
Background....Pages 1-8
Notation....Pages 9-13
Probability Theory: A Refresher....Pages 15-28
Basic Concepts Underlying Simulation....Pages 29-45
Simulation-Based Optimization: An Overview....Pages 47-55
Parametric Optimization: Response Surfaces Neural Networks....Pages 57-91
Parametric Optimization: Simultaneous Perturbation & Meta-Heuristics....Pages 93-132
Control Optimization with Stochastic Dynamic Programming....Pages 133-210
Control Optimization with Reinforcement Learning....Pages 211-275
Control Optimization with Learning Automata....Pages 277-285
Convergence: Background Material....Pages 287-315
Convergence Analysis of Parametric Optimization Methods....Pages 317-342
Convergence Analysis of Control Optimization Methods....Pages 343-408
Case Studies....Pages 409-431
Codes....Pages 433-535
Concluding Remarks....Pages 537-538
Back Matter....Pages 539-554
....