On random coarsening and its applications
Pawan Kumar
TL;DR
This paper introduces a randomized coarsening algorithm leveraging the Poincare separation theorem to improve eigenvalue estimation, optimized for multicore and distributed computing environments.
Contribution
It presents a novel randomized coarsening method that constructs multiple coarse grids for better eigenvalue approximation, suitable for modern parallel processing.
Findings
Enhanced eigenvalue estimates through multiple coarse grids
Algorithm optimized for multicore and distributed systems
Improved computational efficiency in eigenvalue estimation
Abstract
In this paper, we use the Poincare separation theorem for estimating the eigenvalues of the fine grid. We propose a randomized version of the algorithm where several different coarse grids are constructed thus leading to more comprehensive eigenvalue estimates. The proposed algorithm is suited for modern day multicore and distributed processing.
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Taxonomy
TopicsDigital Image Processing Techniques · VLSI and FPGA Design Techniques · Medical Image Segmentation Techniques