Post-Optimal Analysis in Linear Semi-Infinite Optimization [electronic resource] /by Miguel A. Goberna, Marco A. López.
by Goberna, Miguel A [author.]; López, Marco A [author.]; SpringerLink (Online service).
Material type:
BookSeries: SpringerBriefs in Optimization: Publisher: New York, NY : Springer New York : 2014.Description: X, 121 p. 22 illus. in color. online resource.ISBN: 9781489980441.Subject(s): Mathematics | Computer science | Computer software | Mathematics | Operations Research, Management Science | Models and Principles | Programming Techniques | Mathematical SoftwareDDC classification: 519.6 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| T57.6-57.97 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | QA402-402.37 (Browse shelf) | Available |
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.
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.
There are no comments for this item.