Stochastic Adaptive Search for Global Optimization:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2003
|
| Schriftenreihe: | Nonconvex Optimization and Its Applications
72 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algorithms, are gaining in popularity among practitioners and engineers be they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well understood. In this book, an attempt is made to describe the theoretical properties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and development of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical analysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods |
| Beschreibung: | 1 Online-Ressource (XVIII, 224 p) |
| ISBN: | 9781441991829 9781461348269 |
| ISSN: | 1571-568X |
| DOI: | 10.1007/978-1-4419-9182-9 |
Internformat
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| 500 | |a The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algorithms, are gaining in popularity among practitioners and engineers be they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well understood. In this book, an attempt is made to describe the theoretical properties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and development of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical analysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods | ||
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Datensatz im Suchindex
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| author_facet | Zabinsky, Zelda B. |
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| dewey-full | 519.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.6 |
| dewey-search | 519.6 |
| dewey-sort | 3519.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4419-9182-9 |
| format | Electronic eBook |
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| id | DE-604.BV042419329 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:04Z |
| institution | BVB |
| isbn | 9781441991829 9781461348269 |
| issn | 1571-568X |
| language | English |
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| series | Nonconvex Optimization and Its Applications |
| series2 | Nonconvex Optimization and Its Applications |
| spelling | Zabinsky, Zelda B. Verfasser aut Stochastic Adaptive Search for Global Optimization by Zelda B. Zabinsky Boston, MA Springer US 2003 1 Online-Ressource (XVIII, 224 p) txt rdacontent c rdamedia cr rdacarrier Nonconvex Optimization and Its Applications 72 1571-568X The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algorithms, are gaining in popularity among practitioners and engineers be they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well understood. In this book, an attempt is made to describe the theoretical properties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and development of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical analysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods Mathematics Information theory Combinatorics Mathematical optimization Optimization Calculus of Variations and Optimal Control; Optimization Theory of Computation Mathematik Nonconvex Optimization and Its Applications 72 (DE-604)BV010085908 72 https://doi.org/10.1007/978-1-4419-9182-9 Verlag Volltext |
| spellingShingle | Zabinsky, Zelda B. Stochastic Adaptive Search for Global Optimization Nonconvex Optimization and Its Applications Mathematics Information theory Combinatorics Mathematical optimization Optimization Calculus of Variations and Optimal Control; Optimization Theory of Computation Mathematik |
| title | Stochastic Adaptive Search for Global Optimization |
| title_auth | Stochastic Adaptive Search for Global Optimization |
| title_exact_search | Stochastic Adaptive Search for Global Optimization |
| title_full | Stochastic Adaptive Search for Global Optimization by Zelda B. Zabinsky |
| title_fullStr | Stochastic Adaptive Search for Global Optimization by Zelda B. Zabinsky |
| title_full_unstemmed | Stochastic Adaptive Search for Global Optimization by Zelda B. Zabinsky |
| title_short | Stochastic Adaptive Search for Global Optimization |
| title_sort | stochastic adaptive search for global optimization |
| topic | Mathematics Information theory Combinatorics Mathematical optimization Optimization Calculus of Variations and Optimal Control; Optimization Theory of Computation Mathematik |
| topic_facet | Mathematics Information theory Combinatorics Mathematical optimization Optimization Calculus of Variations and Optimal Control; Optimization Theory of Computation Mathematik |
| url | https://doi.org/10.1007/978-1-4419-9182-9 |
| volume_link | (DE-604)BV010085908 |
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