Post-Optimal Analysis in Linear Semi-Infinite Optimization [electronic resource] / by Miguel A. Goberna, Marco A. López.

By:

Goberna, Miguel A [author.]

Contributor(s):

Series: SpringerBriefs in OptimizationPublisher: New York, NY : Springer New York : Imprint: Springer, 2014Description: X, 121 p. 22 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781489980441

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

519.6 23

LOC classification:

QA402-402.37
T57.6-57.97

Online resources:

Click here to access online

Contents:

1. Preliminaries on Linear Semi-Infinite Optimization -- 2. Modeling uncertain Linear Semi-Infinite Optimization problems -- 3. Robust Linear Semi-infinite Optimization -- 4. Sensitivity analysis -- 5. Qualitative stability analysis -- 6. Quantitative stability analysis.

In: Springer eBooksSummary: 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.

Item type:

eBooks

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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.