Mohammed Yusuf Waziri
Papers
2
Total Citations
32
H-Index
2
About
Mohammed Yusuf Waziri is a leading figure in numerical optimization and robotics, specializing in the development of advanced conjugate gradient (CG) methods for solving nonlinear systems of equations. His major contributions lie in enhancing the convergence and efficiency of CG algorithms, particularly through novel parameter selections in the Dai–Liao framework. In his highly cited 2021 work (25 citations), Waziri introduced a new choice for the nonnegative parameter \( t \) in the Dai–Liao method, enabling robust motion control of two-joint planar robotic manipulators. His 2025 study (7 citations) further advanced the field by establishing global convergence properties for self-scaling CG methods applied to monotone nonlinear equations, with direct application to 3DOF arm robot models. This work addresses a long-standing challenge in convergence analysis, bridging theoretical rigor and real-world robotics. Waziri’s research is pivotal for solving large-scale systems in engineering and automation, offering computationally efficient solutions that underpin precise robotic motion. His achievements underscore a commitment to both theoretical depth and practical impact, making him a key contributor to the intersection of numerical analysis and robotics.
Research Focus
Key Achievements
Top Papers
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