Papers
4
Total Citations
79
H-Index
4
About
Sani Aji is a rising figure in numerical optimization and its engineering applications, particularly in robotics. His research centers on developing efficient algorithms for solving nonlinear least-squares (NLS) problems and systems of nonlinear equations, often incorporating inertial effects and nonmonotone search strategies to accelerate convergence. Aji’s major contributions include a structured quasi-Newton algorithm for NLS problems, applied to robotic motion control, and two hybrid spectral methods with inertial effects for solving monotone operator equations, also demonstrated in robotics. His work on a generalized quasi-Newton algorithm based on a structured diagonal Hessian approximation addresses NLS problems with applications to 3DOF planar robot arm manipulators. With over 79 citations across his top papers, Aji’s algorithms are gaining traction for their practical efficiency in real-world robotic systems. His notable achievement includes the development of an inertial-based method for pseudomonotone operator equations, further extending the reach of his optimization techniques. Aji’s research bridges theoretical advances in numerical analysis with tangible solutions in robotics, making his work valuable for students and researchers in computational science and engineering.
Research Focus
Key Achievements
Top Papers
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