Every ℓ1 norm induced by a matrix can be preserved to within 1±ε by rescaling only O(n/ε² log(1/ε)) rows.
Spielman and Shang
5 Pith papers cite this work, alongside 402 external citations. Polarity classification is still indexing.
5
Pith papers citing it
402
external citations · OpenAlex
years
2026 5representative citing papers
Parallel algorithm for matroid basis computation with O(n^{1/3} log^{1/3} n) round complexity, nearly matching the KUW lower bound.
A new algorithm finds a matroid basis in tilde O(n to the 3/7) adaptive rounds via independence oracle.
Every abelian Cayley graph admits an optimal O(ε^{-2} log |G|)-generator weighted Cayley spectral sparsifier, proved via a character-symmetry volume bound on a sparsification polytope.
GSQUEAK produces spectrally accurate sparsifiers for graph Laplacians in a single-pass distributed streaming setting.
citing papers explorer
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Linear-size $\ell_1$ sparsifiers
Every ℓ1 norm induced by a matrix can be preserved to within 1±ε by rescaling only O(n/ε² log(1/ε)) rows.
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A Near-Optimal Parallel Algorithm for Finding Matroid Bases
Parallel algorithm for matroid basis computation with O(n^{1/3} log^{1/3} n) round complexity, nearly matching the KUW lower bound.
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An $\widetilde{O} (n^{3/7})$ Round Parallel Algorithm for Matroid Bases
A new algorithm finds a matroid basis in tilde O(n to the 3/7) adaptive rounds via independence oracle.
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Optimal Sparsifiers for Abelian Cayley Graphs
Every abelian Cayley graph admits an optimal O(ε^{-2} log |G|)-generator weighted Cayley spectral sparsifier, proved via a character-symmetry volume bound on a sparsification polytope.
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Improved large-scale graph learning through ridge spectral sparsification
GSQUEAK produces spectrally accurate sparsifiers for graph Laplacians in a single-pass distributed streaming setting.