Difference between revisions of "Open Problems:45"

Suggested by Robert Krauthgamer Bertinoro 2011 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.

Update

It was shown in [KapralovKS-15,KoganK-15] that estimating the maximum cut to within a factor of $(1-\varepsilon)$ requires $n^{1-O(\varepsilon)}$ space in graph streams. This was further improved in [KapralovKSV-17] who showed that there exists some fixed constant $\varepsilon_*$ for which obtaining a $(1-\varepsilon_*)$ approximation to MAX-CUT requires $\Omega(n)$ space. Moreover, [KapralovKS-15] proved that even in random-ordered streams, $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/2$ approximation.