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Post-Optimal Analysis in Linear Semi-Infinite Optimization

  • Miguel A. Goberna
  • Marco A. López
Book

Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)

Table of contents

  1. Front Matter
    Pages i-x
  2. Miguel A. Goberna, Marco A. López
    Pages 1-22
  3. Miguel A. Goberna, Marco A. López
    Pages 23-37
  4. Miguel A. Goberna, Marco A. López
    Pages 39-49
  5. Miguel A. Goberna, Marco A. López
    Pages 51-60
  6. Miguel A. Goberna, Marco A. López
    Pages 61-77
  7. Miguel A. Goberna, Marco A. López
    Pages 79-107
  8. Back Matter
    Pages 109-121

About this book

Introduction

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.

Keywords

Linear optimization Parametric optimization Semi-infinite optimization Sensitivity analysis Stability analysis Uncertain optimization

Authors and affiliations

  • Miguel A. Goberna
    • 1
  • Marco A. López
    • 2
  1. 1.Statistics and Operations ResearchUniversity of AlicanteAlicanteSpain
  2. 2.Statistics and Operations ResearchUniversity of AlicanteAlicanteSpain

Bibliographic information