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MAX-CUT has a randomized approximation scheme in dense graphs
Random Structures & Algorithms
Szemerédi's regularity lemma for sparse graphs
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STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A spectral technique for random satisfiable 3CNF formulas
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
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Approximating the cut-norm via Grothendieck's inequality
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Optimal Inapproximability Results for Max-Cut and Other 2-Variable CSPs?
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Tensor decomposition and approximation schemes for constraint satisfaction problems
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Recognizing More Unsatisfiable Random k-SAT Instances Efficiently
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A spectral heuristic for bisecting random graphs
Random Structures & Algorithms
Witnesses for non-satisfiability of dense random 3CNF formulas
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Strong Refutation Heuristics for Random k-SAT
Combinatorics, Probability and Computing
Eigenvalues and graph bisection: An average-case analysis
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Quasi-randomness and algorithmic regularity for graphs with general degree distributions
ICALP'07 Proceedings of the 34th international conference on Automata, Languages and Programming
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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.