Stochastic Optimization

By Johannes Schneider , Scott Kirkpatrick

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  • ISBN13: 978-3-5403-4559-6
  • 588 Pages
  • User Level: Science
  • Publication Date: August 6, 2007
  • Available eBook Formats: PDF
  • eBook Price: $129.00
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Full Description
The search for optimal solutions pervades our daily lives. From the scientific point of view, optimization procedures play an eminent role whenever exact solutions to a given problem are not at hand or a compromise has to be sought, e.g. to obtain a sufficiently accurate solution within a given amount of time. This book addresses stochastic optimization procedures in a broad manner, giving an overview of the most relevant optimization philosophies in the first part. The second part deals with benchmark problems in depth, by applying in sequence a selection of optimization procedures to them. While having primarily scientists and students from the physical and engineering sciences in mind, this book addresses the larger community of all those wishing to learn about stochastic optimization techniques and how to use them.
Table of Contents

Table of Contents

  1. General Remarks.
  2. Exact Optimization Algorithms for Simple Problems.
  3. Exact Optimization Algorithms for Complex Problems.
  4. Monte Carlo.
  5. Overview of Optimization Heuristics.
  6. Implementation of Constraints.
  7. Parallelization Strategies.
  8. Construction Heuristics.
  9. Markovian Improvement Heuristics.
  10. Local Search.
  11. Ruin & Recreate.
  12. Simulated Annealing.
  13. Threshold Accepting and Other Algorithms Related to Simulated Annealing.
  14. Changing The Energy Landscape.
  15. Estimation of Expectation Values.
  16. Cooling Techniques.
  17. Estimation of the Calculation Time Needed.
  18. Weakening the Pure Markovian Approach.
  19. Neural Networks.
  20. Genetic Algorithms and Evolution Strategies.
  21. Optimization Algorithms Inspired by Social Animals.
  22. Optimization Algorithms Based on Multi Agent Systems.
  23. Tabu Search.
  24. Histogram Algorithms.
  25. Searching for Backbones.
  26. The Travelling Salesman Problem.
  27. Extensions of the Traveling Salesman Problem.
  28. Application of Construction Heuristics of theTSP.
  29. Local Search Concepts Applied to the TSP.
  30. Next Larger Moves Applied to the TSP.
  31. Ruin and Recreate Applied to the TSP.
  32. Application of Simulated Annealing to the TSP.
  33. Dependencies of the SA
  34. Results on the Moves and the Cooling Process.
  35. Applicaton of Algorithms. Related to Simulated Annealing to the TSP.
  36. Application of Search Space Smoothing to the TSP.
  37. Further Techniques Changing the Energy Landscape of a TSP.
  38. Applicaton of Neural Networks to the TSP. Application of Genetic Algorithms to the TSP.
  39. Social Animal Algorithms Applied to the TSP.
  40. Simulated Trading Applied to the TSP.
  41. Tabu Search Applied to the TSP.
  42. Application of History Algorithms to the TSP.
  43. Application of Searching for Backbones to the TSP.
  44. Simulating Various Types of Government With Searching for Backbones.
  45. The Constraint Satisfaction Problem.
  46. Construction Heuristics for the CSP.
  47. Random Local Iterative Search Heuristics.
  48. Belief Propagation and Survey Propagation.
  49. Outlook for the Future of the Optimization Business.
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