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DOE OSTI · 1843910

Randomized Cholesky Preconditioning for Graph Partitioning Applications

Espinoza, Heliezer J.D.·Boman, Erik G.·Loe, Jennifer
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Abstract

Graph partitioning has emerged as an area of interest due to its use in various applications in computational research. One way to partition a graph is to solve for the eigenvectors of the corresponding graph Laplacian matrix. This project focuses on the eigensolver LOBPCG and the evaluation of a new preconditioner: Randomized Cholesky Factorization (rchol). This proconditioner was tested for its speed and accuracy against other well-known preconditioners for the method. After experiments were run on several known test matrices, rchol appears to be a better preconditioner for structured matrices. This research was sponsored by National Nuclear Security Administration Minority Serving Institutions Internship Program (NNSA-MSIIP) and completed at host facility Sandia National Laboratories. As such, after discussion of the research project itself, this report contains a brief reflection on experience gained as a result of participating in the NNSA-MSIIP.

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97 MATHEMATICS AND COMPUTING

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BibTeXRIS

Espinoza, Heliezer J.D., Boman, Erik G., Loe, Jennifer. 2022-02-07. Randomized Cholesky Preconditioning for Graph Partitioning Applications. https://doi.org/10.2172/1843910

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