Pao-Te Lin
Papers
2
Total Citations
11
H-Index
2
About
Pao-Te Lin is a researcher advancing the intersection of combinatorial optimization and robotics, with a primary focus on submodular maximization problems under routing constraints. His work addresses critical challenges in autonomous systems, including map exploration, environmental monitoring, and spatial search, where tasks must balance coverage efficiency with path-planning constraints. Lin’s key contribution lies in developing novel algorithmic frameworks that tackle the compounded NP-hardness of maximal coverage and traveling salesman problems. His 2023 paper, “Improvement of Submodular Maximization Problems With Routing Constraints via Submodularity and Fourier Sparsity” (7 citations), introduces a generalized cost-benefit approach leveraging submodularity and Fourier sparsity to improve solution quality. Building on this, his 2024 work, “Maximal coverage problems with routing constraints using cross-entropy Monte Carlo tree search” (4 citations), pioneers a hybrid method combining cross-entropy optimization with Monte Carlo tree search, enabling more efficient decision-making in dynamic environments. Though early in his career, Lin’s research has already demonstrated practical impact, offering scalable solutions for real-world robotic applications. His work is particularly notable for bridging theoretical optimization with tangible robotics challenges, making him a promising voice in autonomous systems and algorithmic design.
Research Focus
Key Achievements
Top Papers
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