Arunava Majumder

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About

Arunava Majumder is a researcher whose work sits at the intersection of robotics, numerical optimization, and applied mathematics. His primary research areas include motion control of robotic manipulators, nonlinear equation solving, and the development of efficient conjugate gradient methods. Majumder’s major contribution lies in proposing a novel choice of the nonnegative parameter in the Dai–Liao conjugate gradient method, a critical advancement for solving systems of nonlinear equations. This work has direct applications in robotics, particularly in the motion control of two-joint planar robotic manipulators, where precise and efficient computation is essential. His most-cited paper, “Motion control of the two joint planar robotic manipulators through accelerated Dai–Liao method for solving system of nonlinear equations” (2021), has garnered 25 citations, reflecting its impact on both theoretical optimization and practical robotics. By bridging the gap between abstract numerical methods and real-world robotic control, Majumder has provided tools that enhance the speed and accuracy of robotic motion planning. His research is particularly valuable for students and engineers working on robotic systems that require robust, real-time computational solutions.