Ibrahim Arzuka
Papers
2
Total Citations
33
H-Index
2
About
Ibrahim Arzuka is a rising figure in numerical optimization, whose work focuses on developing efficient algorithms for unconstrained optimization problems. His primary research areas include hybrid conjugate gradient methods and spectral conjugate gradient techniques, with practical applications in robotics and engineering models. Arzuka’s major contribution lies in designing novel hybrid minimization algorithms that improve upon classical methods by optimally selecting parameters from the Dai-Liao conjugacy condition. In his 2023 paper, "A Hybrid Conjugate Gradient Method for Unconstrained Optimization with Application," he introduced a convex combination of Hestenes-Stiefel and Dai-Yuan conjugate gradient algorithms, achieving enhanced convergence and robustness. This work has already garnered 17 citations, reflecting its immediate impact. Complementing this, his "Structured Fletcher-Reeves Spectral Conjugate Gradient Method for Unconstrained Optimization with Application in Robotic Model" (2023, 16 citations) extends spectral conjugate gradient theory to real-world robotic modeling, demonstrating the practical relevance of his theoretical advances. With a total of 33 citations from these two key papers, Arzuka is establishing himself as an innovative contributor to optimization theory, bridging algorithmic design and applied robotics.
Research Focus
Key Achievements
Top Papers
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