Papers
20
Total Citations
427
H-Index
11
About
Poom Kumam is a leading figure in optimization theory and its real-world applications, whose work bridges advanced mathematics with pressing challenges in robotics, finance, and public health. His core research focuses on developing novel conjugate gradient and quasi-Newton algorithms—including spectral, hybrid, and three-term variants—for solving unconstrained optimization and systems of nonlinear monotone equations. Kumam’s contributions are distinguished by their practical impact: his algorithms have been applied to robotic motion control, portfolio selection, image recovery, and even modeling the COVID-19 pandemic. His most cited paper, a 2021 study on a spectral RMIL+ conjugate gradient method (62 citations), demonstrates his ability to refine classical methods for modern computational demands. With over 350 citations across his top ten works, Kumam’s research consistently achieves global convergence guarantees while addressing real-world constraints, such as low-memory requirements for robotic systems. His work on metric dimensions of networks and inertial-based derivative-free methods further showcases his versatility. A prolific collaborator and mentor, Kumam’s algorithms are now standard tools for researchers tackling nonlinear optimization in engineering and data science.
Research Focus
Key Achievements
Top Papers
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- 5Sharp Bounds of Local Fractional Metric Dimensions of Connected Networks34 citations · 2020
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