Ibrahim Mohammed Sulaiman
Sultan Zainal Abidin University, Northern University of Malaysia, Sohar University
Papers
9
Total Citations
177
H-Index
7
About
Ibrahim Mohammed Sulaiman is a leading figure in the development of advanced conjugate gradient (CG) methods for large-scale unconstrained optimization, with a particular focus on real-world applications in robotics, finance, and medical imaging. His most influential work, the "Spectral RMIL+ Conjugate Gradient Method" (2021, 62 citations), introduces a novel spectral parameter that significantly improves convergence and robustness over classical CG algorithms. Sulaiman has also pioneered efficient three-term CG variants (2022, 47 citations) and demonstrated their effectiveness in solving complex problems such as COVID-19 epidemiological modeling and robotic motion control. His research on spectral RMIL methods (2023, 36 citations) further established global convergence properties while extending applications to image recovery. More recently, Sulaiman has advanced self-scaling CG methods for monotone nonlinear equations (2025, 7 citations) and developed preconditioned techniques for robotic arm control (2024). With over 175 total citations across his top papers, his work bridges theoretical optimization with practical engineering, offering computationally efficient, low-memory solutions for autonomous systems and portfolio selection. Sulaiman’s contributions are essential reading for researchers seeking robust, scalable optimization algorithms for real-time control and data-driven modeling.
Research Focus
Key Achievements
Top Papers
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