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Post-Optimal Analysis in Linear Semi-Infinite Optimization

  • Book
  • © 2014

Overview

  • 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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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.

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Table of contents (6 chapters)

Authors and Affiliations

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

    Miguel A. Goberna, Marco A. López

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