Post-Optimal Analysis In Linear Semi-Infinite Optimization (Springerbriefs In Optimization)

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.

• • Author: Miguel A. Goberna, Marco A. Lopez
• • Publisher: Springer
• • Publication Date: Jan 07, 2014
• • Number of Pages: 131 pages
• • Language: English
• • Binding: Paperback
• • ISBN-10: 1489980431
• • ISBN-13: 9781489980434