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Post-Optimal Analysis in Linear Semi-Infinite Optimization (Springerbriefs in Optimization)
Miguel A. Goberna; Marco A. Lopez
Synopsis "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 analysisPost-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. Theauthors 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 andquality 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 aimedtoward researchers, graduate and post-graduate students of mathematics interested in optimization, parametric optimization and related topics.
