## Abstract

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-∈)x^{T} L_{G}x ≤ x^{T}L_{G}x ≤ (1 + ∈)x^{T} L_{G}x, where L_{G} and L_{G} 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(nlog^{3} n/∈^{2}) edges. -An O(mlog log n) time algorithm for graphs with more than nlog^{5} nlog log n edges. -An O(m) algorithm for graphs with more than nlog^{10} n edges. -An O(m) algorithm for unweighted graphs with more than nlog^{8} n edges. These bounds hold up to factors that are in O(poly(log log n)) and are conjectured to be removable.

Original language | English (US) |
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Article number | 17 |

Journal | ACM Transactions on Algorithms |

Volume | 12 |

Issue number | 2 |

DOIs | |

State | Published - Dec 1 2015 |

Externally published | Yes |

## All Science Journal Classification (ASJC) codes

- Mathematics (miscellaneous)

## Keywords

- Spectral sparsification
- Symmetric diagonally dominant (SDD) matrices