An Efficient Sparse Regularity Concept

  • Authors:
  • Amin Coja-Oghlan;Colin Cooper;Alan Frieze

  • Affiliations:
  • acoghlan@inf.ed.ac.uk;ccooper@dcs.kcl.ac.uk;alan@random.math.cmu.edu

  • Venue:
  • SIAM Journal on Discrete Mathematics
  • Year:
  • 2010

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Abstract

Let ${\bf A}$ be a $0/1$ matrix of size $m\times n$, and let $p$ be the density of ${\bf A}$ (i.e., the number of ones divided by $m\cdot n$). We show that ${\bf A}$ can be approximated in the cut norm within $\varepsilon\cdot mnp$ by a sum of cut matrices (of rank 1), where the number of summands is independent of the size $m\cdot n$ of ${\bf A}$, provided that ${\bf A}$ satisfies a certain boundedness condition. This decomposition can be computed in polynomial time. This result extends the work of Frieze and Kannan [Combinatorica, 19 (1999), pp. 175-220] to sparse matrices. As an application, we obtain efficient $1-\varepsilon$ approximation algorithms for “bounded” instances of MAX CSP problems.