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20241006213017.0 |
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m o d |
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210121s2021 sz a ob 000 0 eng d |
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|a SFB
|b eng
|e rda
|e pn
|c SFB
|d GW5XE
|d YDX
|d EBLCP
|d OCLCO
|d OCLCF
|d UKAHL
|d OCLCQ
|d OCLCO
|d COM
|d OCLCQ
|d OCLCO
|d S9M
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| 019 |
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|a 1232411393
|a 1235597773
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| 020 |
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|a 9783030618216
|q (electronic bk.)
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| 020 |
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|a 3030618218
|q (electronic bk.)
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| 020 |
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|z 9783030618209
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| 020 |
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|z 303061820X
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| 024 |
7 |
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|a 10.1007/978-3-030-61821-6
|2 doi
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| 035 |
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|a (OCoLC)1239997042
|z (OCoLC)1232411393
|z (OCoLC)1235597773
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| 050 |
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4 |
|a QA402.5
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| 072 |
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|a PBU
|2 bicssc
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| 072 |
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|a MAT003000
|2 bisacsh
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|a PBU
|2 thema
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| 049 |
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|a HCDD
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| 100 |
1 |
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|a Adelgren, Nathan,
|e author.
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| 245 |
1 |
0 |
|a Advancing parametric optimization :
|b on multiparametric linear complementarity problems with parameters in general locations /
|c Nathan Adelgren.
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| 264 |
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1 |
|a Cham :
|b Springer,
|c [2021]
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| 300 |
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|a 1 online resource (xii, 113 pages) :
|b illustrations (chiefly color)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a SpringerBriefs in optimization,
|x 2190-8354
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| 504 |
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|a Includes bibliographical references.
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| 520 |
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|a The 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.
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| 505 |
0 |
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|a 1. 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.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed March 11, 2021).
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| 650 |
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0 |
|a Mathematical optimization.
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| 650 |
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0 |
|a Geometry, Algebraic.
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| 650 |
|
7 |
|a Optimización matemática
|2 embne
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| 650 |
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7 |
|a Geometría algebraica
|2 embne
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| 650 |
|
7 |
|a Geometry, Algebraic
|2 fast
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| 650 |
|
7 |
|a Mathematical optimization
|2 fast
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| 776 |
0 |
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|z 303061820X
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| 830 |
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0 |
|a SpringerBriefs in optimization.
|x 2190-8354
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-61821-6
|y Click for online access
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| 903 |
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|a SPRING-MATH2021
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| 994 |
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|a 92
|b HCD
|