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Springerbriefs in Optimization Ser.: Advancing Parametric Optimization : On Multiparametric Linear Complementarity Problems with Parameters in General Locations by Nathan Adelgren (2021, Trade Paperback)
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About this product
Product Identifiers
PublisherSpringer International Publishing A&G
ISBN-10303061820X
ISBN-139783030618209
eBay Product ID (ePID)22050396357
Product Key Features
Number of PagesXii, 113 Pages
LanguageEnglish
Publication NameAdvancing Parametric Optimization : On Multiparametric Linear Complementarity Problems with Parameters in General Locations
SubjectGeometry / Algebraic, Optimization
Publication Year2021
TypeTextbook
AuthorNathan Adelgren
Subject AreaMathematics
SeriesSpringerbriefs in Optimization Ser.
FormatTrade Paperback
Dimensions
Item Weight16 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Number of Volumes1 vol.
IllustratedYes
Table Of Content1. Introduction.- 2. Background on mpLCP.- 3. Algebraic Properties of Invariancy Regions.- 4. Phase 2: Partitioning the Parameter Space.- 5. Phase 1: Determining an Initial Feasible Solution.- 6. Further Considerations.- 7. Assessment of Performance.- 8. Conclusion.- Appendix A. Tableaux for Example 2.1.- Appendix B. Tableaux for Example 2.2.- References.
SynopsisThe theory presented in this work merges many concepts from mathematical optimization and real algebraic geometry. When unknown or uncertain data in an optimization problem is replaced with parameters, one obtains a multi-parametric optimization problem whose optimal solution comes in the form of a function of the parameters.The theory and methodology presented in this work allows one to solve both Linear Programs and convex Quadratic Programs containing parameters in any location within the problem data as well as multi-objective optimization problems with any number of convex quadratic or linear objectives and linear constraints. Applications of these classes of problems are extremely widespread, ranging from business and economics to chemical and environmental engineering. Prior to this work, no solution procedure existed for these general classes of problems except for the recently proposed algorithms
LC Classification NumberQA402.5-402.6
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