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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Most of the bounds developed improve those appeared in previously published work.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, the eigenvalue distribution of a family of relaxed mixed constraint preconditioner (RMCP) for the generalized saddle point problems is discussed in detail.
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Key Findings & Data:In this paper, the eigenvalue distribution of a family of relaxed mixed constraint preconditioner (RMCP) for the generalized saddle point problems is discussed in detail.
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Conclusion & Impact:Most of the bounds developed improve those appeared in previously published work.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:saddle point problems · inexact constraint preconditioner · eigenvalue
Abstract
In this paper, the eigenvalue distribution of a family of relaxed mixed constraint preconditioner (RMCP) for the generalized saddle point problems is discussed in detail. Most of the bounds developed improve those appeared in previously published work.
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Cite This Article
Li, C., Wu, S. (2016). Eigenvalue distribution of relaxed mixed constraint preconditioner for saddle point problems. Hacettepe Journal of Mathematics and Statistics, Volume: 45 Issue: 6. https://ajindex.com/articles/1403076