Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers
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Abstract (arXiv)
In this paper we provide an O(m (log log n)O(1) log(1/ε))-expected time algorithm for solving Laplacian systems on n-node m-edge graphs, improving improving upon the previous best expected runtime of O(m √log n (log log n)O(1) log(1/ε)) achieved by (Cohen, Kyng, Miller, Pachocki, Peng, Rao, Xu 2014). To obtain this result we provide efficient constructions of ℓp-stretch graph approximations with improved stretch and sparsity bounds. Additionally, as motivation for this work, we show that for every set of vectors in ℝd (not just those induced by graphs) and all k > 1 there exist ultrasparsifiers with d-1 + O(d/√k) re-weighted vectors of relative condition number at most k. For small k, this improves upon the previous best known relative condition number of tildeO(√k log d), which is only known for the graph case.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 3; 2026: 3.
8 papers.
- 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistancecited by 97
- 2025Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structurecited by 2
- 2025Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nystr"om Methodcited by 1
- 2025Efficient Exact Resistance Distance Computation on Small-Treewidth Graphs: a Labelling Approachcited by 1
- 2021ℓ2-norm Flow Diffusion in Near-Linear Timecited by 0
- 2026Understanding Truncated Positional Encodings for Graph Neural Networkscited by 0
- 2026CAST: Canonical Approximate Schur Tree for Approximate Cholesky on Graphscited by 0
- 2026Support Discovery With Iteratively Reweighted Least Squares for Fixed-Charge Network Flowcited by 0
- 2026Support Discovery With Iteratively Reweighted Least Squares for Fixed-Charge Network Flow8 Sep 2026
- 2026CAST: Canonical Approximate Schur Tree for Approximate Cholesky on Graphs8 Sep 2026
- 2026Understanding Truncated Positional Encodings for Graph Neural Networks11 Jun 2026
- 2025Efficient Exact Resistance Distance Computation on Small-Treewidth Graphs: a Labelling Approach5 Sep 2025
- 2025Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure15 Jul 2025
- 2025Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nystr"om Method21 Jun 2025
- 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistance14 Feb 2023
- 2021ℓ2-norm Flow Diffusion in Near-Linear Time30 May 2021