# Difference between revisions of "Open Problems:45"

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'''Question:''' What about general constraint satisfaction problems with fixed clause-length and alphabet-size? In this case it is even not known how to obtain $O(n \operatorname{polylog} n)$ space bound. | '''Question:''' What about general constraint satisfaction problems with fixed clause-length and alphabet-size? In this case it is even not known how to obtain $O(n \operatorname{polylog} n)$ space bound. | ||

− | == | + | == Updates == |

− | + | The progress on the MAX-CUT problem in the streaming setting: | |

− | + | * Estimating the maximum cut to within a factor of $(1-\varepsilon)$ requires $n^{1-O(\varepsilon)}$ space {{cite|KapralovKS-15|KoganK-15}}. | |

+ | * There exists a constant $\varepsilon_*>0$ such that obtaining a $(1-\varepsilon_*)$ approximation to MAX-CUT requires $\Omega(n)$ space {{cite|KapralovKSV-17}}. | ||

+ | * In random-order streams, $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/2$ approximation {{cite|KapralovKS-15}}. |

## Latest revision as of 04:28, 28 April 2017

Suggested by | Robert Krauthgamer |
---|---|

Source | Bertinoro 2011 |

Short link | https://sublinear.info/45 |

The problem is defined as follows: given a stream of edges of an $n$-node graph $G$, estimate the value of the maximum cut in $G$.

**Question:** Is there an algorithm with an approximation factor strictly better than $1/2$ that uses $o(n)$ space?

**Background:** Note that $1/2$ is achievable using random assignment
argument. Moreover, using sparsification arguments [Trevisan-09,AhnG-09], one can
obtain a better approximation ratio using $O(n \operatorname{polylog} n)$ space.
Woodruff and Bhattacharyya (private communication) noted that
subsampling $O(n/\epsilon^2)$ edges gives, with high probability, an
$\epsilon$-additive approximation for all cuts, and thus
$1+\epsilon$ multiplicative approximation for MAX-CUT.

**Question:** What about general constraint satisfaction problems with fixed clause-length and alphabet-size? In this case it is even not known how to obtain $O(n \operatorname{polylog} n)$ space bound.

## Updates[edit]

The progress on the MAX-CUT problem in the streaming setting:

- Estimating the maximum cut to within a factor of $(1-\varepsilon)$ requires $n^{1-O(\varepsilon)}$ space [KapralovKS-15,KoganK-15].
- There exists a constant $\varepsilon_*>0$ such that obtaining a $(1-\varepsilon_*)$ approximation to MAX-CUT requires $\Omega(n)$ space [KapralovKSV-17].
- In random-order streams, $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/2$ approximation [KapralovKS-15].