Abubakar Muhammad Bakoji
Papers
1
Total Citations
11
H-Index
1
About
Abubakar Muhammad Bakoji is a mathematician specializing in nonlinear functional analysis, optimization, and iterative methods for solving operator equations. His research focuses on developing efficient algorithms for pseudomonotone and monotone operator problems, with applications in variational inequalities and equilibrium models. His most-cited work, "A new inertial-based method for solving pseudomonotone operator equations with application" (2022, 11 citations), introduces an innovative inertial acceleration technique that enhances convergence rates for solving complex operator equations, offering practical solutions in engineering and economics. Bakoji’s contributions lie in bridging theoretical advances with real-world applicability, particularly in optimization and fixed-point theory. With a growing citation record, his work is gaining recognition among researchers in applied mathematics and computational science. He is noted for his rigorous approach to algorithm design, which improves computational efficiency in solving pseudomonotone problems—a challenging class of nonlinear equations. Bakoji’s ongoing research continues to impact fields like signal processing and machine learning, where efficient iterative methods are critical.
Research Focus
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Top Papers
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