Mahmoud Muhammad Yahaya
Papers
7
Total Citations
107
H-Index
7
About
Mahmoud Muhammad Yahaya is a rising figure in numerical optimization, whose work is reshaping how we solve nonlinear least-squares (NLS) problems and systems of nonlinear equations—with direct, real-world applications in robotics. His research centers on developing efficient, structured algorithms that accelerate convergence without sacrificing stability. Yahaya’s major contributions include a suite of novel quasi-Newton and spectral methods that cleverly incorporate inertial effects and structured Hessian approximations, dramatically improving performance on complex problems like robotic arm motion control. His most cited work, "A structured quasi-Newton algorithm with nonmonotone search strategy for structured NLS problems and its application in robotic motion control" (2021, 26 citations), exemplifies this approach. Another highly influential paper introduces two hybrid spectral methods with inertial effects for solving nonlinear monotone equations (2021, 23 citations). Yahaya’s algorithms have been successfully applied to models ranging from 3DOF to 4DOF planar robot arm manipulators, demonstrating their practical power. With multiple papers published between 2021 and 2024, his work is gaining rapid traction, establishing him as a key innovator at the intersection of optimization theory and robotic control.
Research Focus
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Top Papers
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