Rabiu Bashir Yunus
Papers
3
Total Citations
25
H-Index
3
About
Rabiu Bashir Yunus is a rising figure in computational optimization, whose work bridges theoretical mathematics and practical robotics. His research centers on developing advanced conjugate gradient (CG) algorithms—specifically, structured spectral and three-term CG methods—to solve nonlinear least-squares (NLS) problems and monotone nonlinear equations. A key contribution is his modification of the Hestenes-Stiefel method, introducing a spectral parameter derived from a modified secant relation, which eliminates the need for safeguarding steps while ensuring robust convergence. His 2023 paper on this topic has already garnered 10 citations, reflecting its immediate impact. In 2024, Yunus extended this work with two novel three-term CG algorithms that incorporate second-order curvature information, achieving improved conjugacy and sufficient descent—critical for handling complex NLS problems like those in a 4DOF arm robot model (8 citations). Most recently, his 2025 study on self-scaling CG methods for monotone equations, applied to a 3DOF arm robot model (7 citations), addresses the long-standing challenge of global convergence in CG methods. Yunus’s work is notable for its direct application to robot arm control, demonstrating how theoretical optimization can enhance real-world motion planning and precision. His growing citation record signals a promising career at the intersection of numerical analysis and robotics.
Research Focus
Key Achievements
Top Papers
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