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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Then, last problem is solved by coordinate descent method
📌 3 Key Takeaways
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Objective & Hypothesis:In the article, a numerical method for convex programming problem (with linear inequality) in Hilbert spaces is given.
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Key Findings & Data:Firstly, by Khun-Tuckerconditions problem is reduced to minimize a convex funtional under nonnegative variables.
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Conclusion & Impact:Then, last problem is solved by coordinate descent method
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Convex function · Khun-Tucker conditions · minimization method
Abstract
In the article, a numerical method for convex programming problem (with linear inequality) in Hilbert spaces is given. Firstly, by Khun-Tuckerconditions problem is reduced to minimize a convex funtional under nonnegative variables. Then, last problem is solved by coordinate descent method
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Cite This Article
AHMETOĞLU, F. (2013). CALCULATION METHOD FOR QUADRATIC PROGRAMMING PROBLEM IN HILBERT SPACES, PARTIALLY ORDERED BY CONE WITH EMPTY INTERIOR. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Cilt: 62 - Sayı: 2, 11–15. https://ajindex.com/articles/747391