Faster spectral sparsification and numerical algorithms for SDD matrices

Ioannis Koutis, Alex Levin, Richard Peng

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


We study algorithms for spectral graph sparsification. The input is a graph G with n vertices and medges, and the output is a sparse graph G that approximates G in an algebraic sense. Concretely, for all vectors x and any ∈ > 0, the graph G satisfies (1-∈)xT LGx ≤ xTLGx ≤ (1 + ∈)xT LGx, where LG and LG are the Laplacians of G and G, respectively. The first contribution of this article applies to all existing sparsification algorithms that rely on solving solving linear systems on graph Laplacians. These algorithms are the fastest known to date. Specifically, we show that less precision is required in the solution of the linear systems, leading to speedups by an O(log n) factor. We also present faster sparsification algorithms for slightly dense graphs: -An O(mlog n) time algorithm that generates a sparsifier with O(nlog3 n/∈2) edges. -An O(mlog log n) time algorithm for graphs with more than nlog5 nlog log n edges. -An O(m) algorithm for graphs with more than nlog10 n edges. -An O(m) algorithm for unweighted graphs with more than nlog8 n edges. These bounds hold up to factors that are in O(poly(log log n)) and are conjectured to be removable.

Original languageEnglish (US)
Article number17
JournalACM Transactions on Algorithms
Issue number2
StatePublished - Dec 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Mathematics (miscellaneous)


  • Spectral sparsification
  • Symmetric diagonally dominant (SDD) matrices


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