## Introduction to Stochastic Search and Optimization: Estimation, Simulation, and ControlA unique interdisciplinary foundation for real-world problemsolving Stochastic search and optimization techniques are used in a vastnumber of areas, including aerospace, medicine, transportation, andfinance, to name but a few. Whether the goal is refining the designof a missile or aircraft, determining the effectiveness of a newdrug, developing the most efficient timing strategies for trafficsignals, or making investment decisions in order to increaseprofits, stochastic algorithms can help researchers andpractitioners devise optimal solutions to countless real-worldproblems. Introduction to Stochastic Search and Optimization: Estimation,Simulation, and Control is a graduate-level introduction to theprinciples, algorithms, and practical aspects of stochasticoptimization, including applications drawn from engineering,statistics, and computer science. The treatment is both rigorousand broadly accessible, distinguishing this text from much of thecurrent literature and providing students, researchers, andpractitioners with a strong foundation for the often-daunting taskof solving real-world problems. The text covers a broad range of today’s most widely usedstochastic algorithms, including: - Random search
- Recursive linear estimation
- Stochastic approximation
- Simulated annealing
- Genetic and evolutionary methods
- Machine (reinforcement) learning
- Model selection
- Simulation-based optimization
- Markov chain Monte Carlo
- Optimal experimental design
The book includes over 130 examples, Web links to software anddata sets, more than 250 exercises for the reader, and an extensivelist of references. These features help make the text an invaluableresource for those interested in the theory or practice ofstochastic search and optimization. |

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Auf Seite 509 und 510 gute Erklärung wann man Differenzierung und Integral tauschen kann.

### Contents

1 | |

2 Direct Methods for Stochastic Search | 34 |

3 Recursive Estimation for Linear Models | 65 |

4 Stochastic Approximation for Nonlinear RootFinding | 95 |

5 Stochastic Gradient Form of Stochastic Approximation | 126 |

6 Stochastic Approximation and the FiniteDifference Method | 150 |

7 Simultaneous Perturbation Stochastic Approximation | 176 |

8 AnnealingType Algorithms | 208 |

15 SimulationBased Optimization II Stochastic Gradient and Sample Path Methods | 409 |

16 Markov Chain Monte Carlo | 436 |

17 Optimal Design for Experimental Inputs | 464 |

Appendix A Selected Results from Multivariate Analysis | 505 |

Appendix B Some Basic Tests in Statistics | 515 |

Appendix C Probability Theory and Convergence | 526 |

Appendix D Random Number Generation | 538 |

Appendix E Markov Processes | 547 |

9 Evolutionary Computation I Genetic Algorithms | 231 |

10 Evolutionary Computation 11 General Methods and Theory | 259 |

11 Reinforcement Learning via Temporal Differences | 278 |

12 Statistical Methods for Optimization in Discrete Problems | 300 |

13 Model Selection and Statistical Information | 329 |

14 SimulationBased Optimization I Regeneration Common Random Numbers and Selection Methods | 367 |

Answers to Selected Exercises | 552 |

558 | |

Frequently Used Notation | 580 |

583 | |

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Introduction to Stochastic Search and Optimization: Estimation, Simulation ... James C. Spall No preview available - 2005 |