Stochastic Optimization

  • Johannes Josef Schneider
  • Scott Kirkpatrick

Part of the Scientific Computation book series (SCIENTCOMP)

Table of contents

  1. Front Matter
    Pages I-XVI
  2. Theory Overview of Stochastic Optimization Algorithms

  3. Applications

    1. Front Matter
      Pages 199-199
    2. Pages 201-207
  4. The Traveling Salesman Problem

  5. The Constraint Satisfaction Problem

    1. Front Matter
      Pages 500-500

About this book


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.


Constraint Satisfaction Markov Monte Carlo Random Numbers Simulated Annealing Stochastic Optimization Tabu Search Traveling Salesman Problem algorithm algorithms construction genetic algorithms optimization

Authors and affiliations

  • Johannes Josef Schneider
    • 1
  • Scott Kirkpatrick
    • 2
  1. 1.Institut für Physik Fachbereich 08 Physik, Mathematik und InformatikJohannes Gutenberg-Universität MainzMainzGermany
  2. 2.The Selim and Rachel Benin School of Engineering and Computer ScienceThe Hebrew University of JerusalemJerusalemIsrael

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