Samsul Ariffin Abdul Karim

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About

Samsul Ariffin Abdul Karim is a leading figure in numerical optimization and control theory, whose work has significantly advanced the solution of nonlinear least-squares (NLS) problems. His research focuses on developing novel conjugate gradient (CG) algorithms that incorporate second-order curvature information, bridging the gap between theoretical mathematics and practical engineering applications. In his highly cited 2023 paper, Karim introduced a modified structured spectral Hestenes-Stiefel (HS) method, which cleverly devises a spectral parameter using a modified secant relation—a breakthrough that eliminates the need for a safe guard in implementation, resulting in more robust and efficient convergence. Building on this, his 2024 work presents two new three-term conjugate gradient (TTCG) algorithms that leverage the structured secant equation, ensuring improved conjugacy and sufficient descent conditions. With over 18 citations in just two years, these contributions are gaining rapid traction. Notably, Karim demonstrates the real-world impact of his algorithms by applying them to robot arm control, specifically a 4DOF arm model, showcasing how his theoretical innovations directly enhance the precision and stability of robotic systems. His work stands as a vital resource for researchers in optimization, robotics, and applied mathematics.