Kushal Chakrabarti
Papers
1
Total Citations
2
H-Index
1
About
Kushal Chakrabarti is a researcher focused on advancing optimization algorithms, particularly in the realm of iterative methods for solving large-scale problems. His primary research areas include gradient-descent techniques, preconditioning strategies, and momentum-based acceleration in numerical optimization. Chakrabarti's major contribution lies in his work on the Iteratively Preconditioned Gradient-Descent (IPG) algorithm, where he introduced momentum formulations to enhance convergence speed and stability. His 2023 paper, "Accelerating the Iteratively Preconditioned Gradient-Descent Algorithm using Momentum," proposes three novel approaches that integrate momentum terms, demonstrating improved performance over prior results. Though early in his career with 2 citations to date, this work represents a significant step in bridging classical optimization methods with modern acceleration techniques. Chakrabarti's research holds promise for applications in machine learning, signal processing, and scientific computing, where efficient solvers are critical. His methodical approach to refining gradient-based algorithms underscores his potential to contribute meaningfully to the field of computational mathematics.
Research Focus
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Top Papers
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