4 papers
Self-Supervised Learning for Sparse Matrix Reordering
Ziwei Li, Tao Yuan, Fangfang Liu +3
Rearranging the rows or columns of a sparse matrix using an appropriate ordering can significantly reduce fill-ins, i.e., new nonzeros introduced during matrix factorization, decre…
Learning Fill-in Reduction Ordering via Graph Policy Optimization for Sparse Matrices
Ziwei Li, Shuzi Niu, Huiyuan Li +2
Matrix reordering in large sparse solvers seeks a permutation that minimizes factorization fill-in to reduce memory and computation. Because the minimum fill-in ordering problem is…
Bridging the Gap between Sparse Matrix Reordering and Factorization: A Deep Learning Framework for Fill-in Reduction
Ziwei Li, Tao Yuan, Shuzi Niu +1
Sparse matrix reordering can significantly reduce the fill-in during matrix factorization, thereby decreasing the computational and storage requirements in sparse matrix computatio…
Factorization-in-Loop: Proximal Fill-in Minimization for Sparse Matrix Reordering
Ziwei Li, Shuzi Niu, Tao Yuan +2
Fill-ins are new nonzero elements in the summation of the upper and lower triangular factors generated during LU factorization. For large sparse matrices, they will increase the me…