A self-supervised multigrid graph network with triplet sampling from the Fill-Path Theorem and an end-max chain loss reduces fill-ins and speeds up LU factorization on SuiteSparse matrices.
T., Pothen, A., Simon, H
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A GNN framework learns spectral embeddings of sparse matrices to minimize a fill-in surrogate and produces competitive reorderings versus classical graph algorithms.
citing papers explorer
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Self-Supervised Learning for Sparse Matrix Reordering
A self-supervised multigrid graph network with triplet sampling from the Fill-Path Theorem and an end-max chain loss reduces fill-ins and speeds up LU factorization on SuiteSparse matrices.
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Bridging the Gap between Sparse Matrix Reordering and Factorization: A Deep Learning Framework for Fill-in Reduction
A GNN framework learns spectral embeddings of sparse matrices to minimize a fill-in surrogate and produces competitive reorderings versus classical graph algorithms.