Papers
4
Total Citations
76
H-Index
4
About
Nasiru Salihu is a rising figure in numerical optimization, whose work focuses on developing efficient conjugate gradient (CG) methods for unconstrained optimization. His core contributions lie in improving the convergence and practical performance of spectral CG algorithms—critical tools for solving large-scale problems in engineering and data science. Salihu’s most cited paper (2023, 36 citations) addresses the global convergence of the spectral RMIL method, a variant of the classic RMIL algorithm, by correcting earlier theoretical flaws and demonstrating its effectiveness in robotic modeling and image recovery. He has also introduced hybrid CG methods that cleverly combine the Hestenes-Stiefel and Dai-Yuan algorithms, achieving robust performance across diverse applications. With additional work on structured Fletcher-Reeves spectral methods and combined techniques for robotic and image restoration models, Salihu’s research bridges rigorous convergence theory with real-world impact. His papers, accumulating over 75 citations, are widely referenced by researchers seeking reliable, scalable optimization algorithms. Salihu’s ability to identify and resolve theoretical gaps in established methods, while validating them on practical benchmarks, marks him as a thoughtful and impactful contributor to the optimization community.
Research Focus
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Top Papers
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