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Post-Optimal Analysis in Linear Semi-Infinite Optimization

  • Book
  • © 2014

Overview

Authors:
  1. Miguel A. Goberna

    1. Statistics and Operations Research, University of Alicante, Alicante, Spain

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  2. Marco A. López

    1. Statistics and Operations Research, University of Alicante, Alicante, Spain

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    You can also search for this author in PubMed   Google Scholar

  • Depicts modeling uncertainty, qualitative stability analysis, quantitative stability analysis and sensitivity analysis in relation to linear semi-infinite optimization
  • Emphasizes main concepts, results and technical aspects of linear semi-infinite optimization to readers in various fields
  • Contains recent results on the emerging quantitative stability and sensitivity theories

Part of the book series: SpringerBriefs in Optimization (BRIEFSOPTI)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-x
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  2. Preliminaries on Linear Semi-infinite Optimization

    • Miguel A. Goberna, Marco A. López
    Pages 1-22

  3. Modeling Uncertain Linear Semi-infinite Optimization Problems

    • Miguel A. Goberna, Marco A. López
    Pages 23-37

  4. Robust Linear Semi-infinite Optimization

    • Miguel A. Goberna, Marco A. López
    Pages 39-49

  5. Sensitivity Analysis

    • Miguel A. Goberna, Marco A. López
    Pages 51-60

  6. Qualitative Stability Analysis

    • Miguel A. Goberna, Marco A. López
    Pages 61-77

  7. Quantitative Stability Analysis

    • Miguel A. Goberna, Marco A. López
    Pages 79-107

  8. Back Matter

    Pages 109-121
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Keywords

About this book

Post-Optimal Analysis in Linear Semi-Infinite Optimization examines the following topics in regards to linear semi-infinite optimization: modeling uncertainty, qualitative stability analysis, quantitative stability analysis and sensitivity analysis. Linear semi-infinite optimization (LSIO) deals with linear optimization problems where the dimension of the decision space or the number of constraints is infinite. The authors compare the post-optimal analysis with alternative approaches to uncertain LSIO problems and provide readers with criteria to choose the best way to model a given uncertain LSIO problem depending on the nature and quality of the data along with the available software. This work also contains open problems which readers will find intriguing a challenging. Post-Optimal Analysis in Linear Semi-Infinite Optimization is aimed toward researchers, graduate and post-graduate students of mathematics interested in optimization, parametric optimization and related topics.

Authors and Affiliations

  • Statistics and Operations Research, University of Alicante, Alicante, Spain

    Miguel A. Goberna, Marco A. López

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