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Simulation and Optimization

Proceedings of the International Workshop on Computationally Intensive Methods in Simulation and Optimization held at the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria, August 23–25, 1990

  • Conference proceedings
  • © 1992

Overview

Editors:
  1. Georg Pflug

    1. Institut für Statistik und Informatik, Universität Wien, Wien, Austria

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  2. Ulrich Dieter

    1. Institut für Statistik, Technische Universität Graz, Graz, Austria

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Part of the book series: Lecture Notes in Economics and Mathematical Systems (LNE, volume 374)

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Table of contents (12 papers)

  1. Front Matter

    Pages I-X
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  4. Random Numbers

    1. Nonuniform Random Numbers: A Sensitivity Analysis for Transformation Methods

      • Lothar Afflerbach, Wolfgang Hörmann
      Pages 135-144

    2. Nonlinear Methods for Pseudorandom Number and Vector Generation

      • Harald Niederreiter
      Pages 145-153

    3. Sampling from Discrete and Continuous Distributions with C-Rand

      • Ernst Stadlober, Ralf Kremer
      Pages 154-162

  5. Back Matter

    Pages 163-166
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Keywords

About this book

This volume contains selected papers presented at the "International Workshop on Computationally Intensive Methods in Simulation and Op­ th th timization" held from 23 to 25 August 1990 at the International Institute for Applied Systems Analysis (nASA) in La~enburg, Austria. The purpose of this workshop was to evaluate and to compare recently developed methods dealing with optimization in uncertain environments. It is one of the nASA's activities to study optimal decisions for uncertain systems and to make the result usable in economic, financial, ecological and resource planning. Over 40 participants from 12 different countries contributed to the success of the workshop, 12 papers were selected for this volume. Prof. A. Kurzhanskii Chairman of the Systems and Decision Sciences Program nASA Preface Optimization in an random environment has become an important branch of Applied Mathematics and Operations Research. It deals with optimal de­ cisions when only incomplete information of t.he future is available. Consider the following example: you have to make the decision about the amount of production although the future demand is unknown. If the size of the de­ mand can be described by a probability distribution, the problem is called a stochastic optimization problem.

Editors and Affiliations

  • Institut für Statistik und Informatik, Universität Wien, Wien, Austria

    Georg Pflug

  • Institut für Statistik, Technische Universität Graz, Graz, Austria

    Ulrich Dieter

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