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Journals / Journal of Multidisciplinary Modeling and Optimization / 2021 / Cilt: 4 Sayı: 2

Modified Hestenes - Stiefel Conjugate Gradient (MHS-CG) method for solving unconstrained optimization

Osama Alkassaab(University of Mosul , College of Basic Education)
Khalil K. Abbo(University of Telafer)
Ibrahim Saleh(University of Mosul)
Pages
32–42
Publication Date
2023-01-03
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results from the numerical evaluations demonstrate that the new technique is very efficient in practical computing and outperforms previous comparable approaches in a wide range of conditions

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The conjugate gradient technique is one of the most effective methods for solving and minimizing unconstrained optimization problems, and it is widely utilized.
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Key Findings & Data:In this research, we introduce a novel nonlinear conjugate gradient approach with excellent convergence for unconstrained minimization problems that is based on the nonlinear conjugate gradient method.
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Conclusion & Impact:Results from the numerical evaluations demonstrate that the new technique is very efficient in practical computing and outperforms previous comparable approaches in a wide range of conditions
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:unconstrained optimization · nonlinear conjugate gradient method · global convergence

Abstract

The conjugate gradient technique is one of the most effective methods for solving and minimizing unconstrained optimization problems, and it is widely utilized. In this research, we introduce a novel nonlinear conjugate gradient approach with excellent convergence for unconstrained minimization problems that is based on the nonlinear conjugate gradient method. The new algorithm has the property of descent as well as global convergence. Results from the numerical evaluations demonstrate that the new technique is very efficient in practical computing and outperforms previous comparable approaches in a wide range of conditions

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Cite This Article

Alkassaab, O., Abbo, K. K., Saleh, I. (2023). Modified Hestenes - Stiefel Conjugate Gradient (MHS-CG) method for solving unconstrained optimization. Journal of Multidisciplinary Modeling and Optimization, Cilt: 4 Sayı: 2, 32–42. https://ajindex.com/articles/938429

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