Stochastic Optimization

1. Auflage. Softcover version of original hardcover Auflage 2006
ISBN: 978-3-642-07094-5
Verlag: Springer

Buch, Englisch, 568 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 873 g

Reihe: Scientific Computation

This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.

Zielgruppe

Research

Autoren/Hrsg.

Kirkpatrick, Scott

Schneider, Johannes

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
• Mathematik | Informatik Mathematik Operations Research Spieltheorie
• Mathematik | Informatik Mathematik Stochastik Elementare Stochastik
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure

Inhaltsverzeichnis

Theory Overview of Stochastic Optimization Algorithms.- General Remarks.- Exact Optimization Algorithms for Simple Problems.- Exact Optimization Algorithms for Complex Problems.- Monte Carlo.- Overview of Optimization Heuristics.- Implementation of Constraints.- Parallelization Strategies.- Construction Heuristics.- Markovian Improvement Heuristics.- Local Search.- Ruin & Recreate.- Simulated Annealing.- Threshold Accepting and Other Algorithms Related to Simulated Annealing.- Changing the Energy Landscape.- Estimation of Expectation Values.- Cooling Techniques.- Estimation of Calculation Time Needed.- Weakening the Pure Markovian Approach.- Neural Networks.- Genetic Algorithms and Evolution Strategies.- Optimization Algorithms Inspired by Social Animals.- Optimization Algorithms Based on Multiagent Systems.- Tabu Search.- Histogram Algorithms.- Searching for Backbones.- Applications.- General Remarks.- The Traveling Salesman Problem.- The Traveling Salesman Problem.- Extensions of Traveling Salesman Problem.- Application of Construction Heuristics to TSP.- Local Search Concepts Applied to TSP.- Next Larger Moves Applied to TSP.- Ruin & Recreate Applied to TSP.- Application of Simulated Annealing to TSP.- Dependencies of SA Results on Moves and Cooling Process.- Application to TSP of Algorithms Related to Simulated Annealing.- Application of Search Space Smoothing to TSP.- Further Techniques Changing the Energy Landscape of a TSP.- Application of Neural Networks to TSP.- Application of Genetic Algorithms to TSP.- Social Animal Algorithms Applied to TSP.- Simulated Trading Applied to TSP.- Tabu Search Applied to TSP.- Application of History Algorithms to TSP.- Application of Searching for Backbones to TSP.- Simulating Various Types of Government with Searching for Backbones.- The Constraint Satisfaction Problem.- The Constraint Satisfaction Problem.- Construction Heuristics for CSP.- Random Local Iterative Search Heuristics.- Belief Propagation and Survey Propagation.- Outlook.- Future Outlook of Optimization Business.