arXiv (All)
open access
CAST: Canonical Approximate Schur Tree for Approximate Cholesky on Graphs
Graph-data workloads such as diffusion estimation, ranking, semi-supervised learning, and network optimization often solve many Laplacian or symmetric diagonally dominant M-matrix (SDDM) systems with the same coefficient matrix. Approximate Cholesky preconditioners eliminate vertices one at a time a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Polynomial Lower Bounds for Distributed Graph Sketching with Tiny Error: Connectivity and Spanning Tree Construction#973425
- Graph4BiLO: Graph Neural Network Approximation for Bilevel Mixed-Integer Linear Optimization#799333
- Graph4BiLO: Graph Neural Network Approximation for Bilevel Mixed-Integer Linear Optimization#797910
- RDT based upper bounds on the largest average submatrix values#1012503
- Flexible latent variable models on graphs: Laplace approximated inference for multiview network data#1062386
Record · ID 987020
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive