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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The lexicographic duality is then applied for problems with polynomials in the objectives and in the constraints.
📌 3 Key Takeaways
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Objective & Hypothesis:The paper presents the Kuhn-Tucker conditions and duality theory for the lexicographic minimization problem: iex m in{F (x)|x e R n,G (x ) < O mj where F and G are vector valued functions form R„ -~*RS and Rn —>Rm , respectively.
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Key Findings & Data:The paper presents the Kuhn-Tucker conditions and duality theory for the lexicographic minimization problem: iex m in{F (x)|x e R n,G (x ) < O mj where F and G are vector valued functions form R„ -~*RS and Rn —>Rm , respectively.
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Conclusion & Impact:The lexicographic duality is then applied for problems with polynomials in the objectives and in the constraints.
🔬Design:Çeviri
📍Scope:Comprehensive Sample
⚙️Variables:Leksikografi · Geometri · Programlama
Özet
The paper presents the Kuhn-Tucker conditions and duality theory for the lexicographic minimization problem: iex m in{F (x)|x e R n,G (x ) < O mj where F and G are vector valued functions form R„ -~*RS and Rn —>Rm , respectively. The lexicographic duality is then applied for problems with polynomials in the objectives and in the constraints.
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Cite This Article
Luptacik, M., Turnovec, F. (1996). LEKSIKOGRAFIK GEOMETRİK PROGRAMLAMA (LGP). Öneri Dergisi, Cilt: 1 Sayı: 4, 153–156. https://doi.org/10.14783/maruoneri.713024