Aliyu Muhammed Awwal
King Mongkut's University of Technology Thonburi, Gombe State University, China Medical University
Papers
12
Total Citations
302
H-Index
10
About
Aliyu Muhammed Awwal is a leading figure in optimization algorithms, specializing in conjugate gradient (CG) and quasi-Newton methods for solving unconstrained and nonlinear systems. His core contributions lie in developing efficient, low-memory iterative techniques with real-world applications in portfolio selection, image recovery, and robotic motion control. Awwal’s work on spectral RMIL+ conjugate gradient methods (62 citations) and three-term CG algorithms (47 citations) has advanced the global convergence theory of these solvers, addressing long-standing issues like the Dai (2016) abnormality in convergence results. He has also pioneered inertial-based derivative-free methods for monotone nonlinear equations (30 citations) and structured quasi-Newton algorithms for nonlinear least-squares problems, notably applied to 3DOF planar robot arm manipulators. His research consistently bridges theoretical rigor with practical impact, as seen in his work on COVID-19 modeling and two-joint robotic manipulators. With over 300 total citations across his top papers, Awwal is recognized for accelerating algorithmic performance through inertial effects and spectral parameters, making his methods indispensable for engineering and data science optimization challenges.
Research Focus
Key Achievements
Top Papers
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