Difference between revisions of "Open Problems:45"
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== Updates == | == Updates == | ||
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| + | (''Unless explicitly mentioned, the stream is adversarial'') | ||
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The progress on the MAX-CUT problem in the streaming setting: | 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}}. | * 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}}. | * 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}}. | * In random-order streams, $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/2$ approximation {{cite|KapralovKS-15}}. | ||
| − | * | + | * $\Omega(n)$ space is needed to obtain a better than $1/2$ approximation {{cite|KapralovK-19}}. |
The progress on general constraint satisfaction problems in the streaming setting: | The progress on general constraint satisfaction problems in the streaming setting: | ||
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* For every $\varepsilon>0$, there is an $O(\log n)$ space $(2/5-\varepsilon)$-approximation linear sketching algorithm for MAX-2AND {{cite|GuruswamiVV-17}}. | * For every $\varepsilon>0$, there is an $O(\log n)$ space $(2/5-\varepsilon)$-approximation linear sketching algorithm for MAX-2AND {{cite|GuruswamiVV-17}}. | ||
* $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/2$ approximation for MAX-DICUT (also MAX-2AND) {{cite|GuruswamiVV-17}}. | * $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/2$ approximation for MAX-DICUT (also MAX-2AND) {{cite|GuruswamiVV-17}}. | ||
| − | * Dichotomy theorem for streaming approximation of all Boolean Max-2CSPs: For every Boolean Max-2CSP, there is an explicit constant $\alpha$ such that for every $\varepsilon$, (i) there is an $O(\log n)$ space $(\alpha-\varepsilon)$-approximation linear sketching algorithm and (ii) $\Omega(\sqrt{n})$ space is needed to obtain a better than $\alpha$ approximation {{cite|ChouGV-20}}. | + | * Dichotomy theorem for streaming approximation of all Boolean Max-2CSPs: For every Boolean Max-2CSP, there is an explicit constant $\alpha$ such that for every $\varepsilon$, (i) there is an $O(\log n)$ space $(\alpha-\varepsilon)$-approximation linear sketching algorithm and (ii) $\Omega(\sqrt{n})$ space is needed to obtain a better than $\alpha$ streaming approximation {{cite|ChouGV-20}}. |
| − | * For every $\varepsilon>0$, there is an $O(\log n)$ space $(4/9-\varepsilon)$-approximation linear sketching algorithm for MAX-DICUT; $\Omega(\sqrt{n})$ space is needed to obtain a better than $4/9$ approximation for MAX- | + | * For every $\varepsilon>0$, there is an $O(\log n)$ space $(4/9-\varepsilon)$-approximation linear sketching algorithm for MAX-DICUT; $\Omega(\sqrt{n})$ space is needed to obtain a better than $4/9$ streaming approximation {{cite|ChouGV-20}}. |
| + | * For every $\varepsilon>0$, there is an $O(\log n)$ space $(1/\sqrt{2}-\varepsilon)$-approximation linear sketching algorithm for MAX-$k$SAT; $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/\sqrt{2}$ streaming approximation {{cite|ChouGV-20}}. | ||
Revision as of 04:55, 27 June 2022
| 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
(Unless explicitly mentioned, the stream is adversarial)
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].
- $\Omega(n)$ space is needed to obtain a better than $1/2$ approximation [KapralovK-19].
The progress on general constraint satisfaction problems in the streaming setting:
- For every $\varepsilon>0$, there is an $O(\log n)$ space $(2/5-\varepsilon)$-approximation linear sketching algorithm for MAX-DICUT [GuruswamiVV-17].
- For every $\varepsilon>0$, there is an $O(\log n)$ space $(2/5-\varepsilon)$-approximation linear sketching algorithm for MAX-2AND [GuruswamiVV-17].
- $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/2$ approximation for MAX-DICUT (also MAX-2AND) [GuruswamiVV-17].
- Dichotomy theorem for streaming approximation of all Boolean Max-2CSPs: For every Boolean Max-2CSP, there is an explicit constant $\alpha$ such that for every $\varepsilon$, (i) there is an $O(\log n)$ space $(\alpha-\varepsilon)$-approximation linear sketching algorithm and (ii) $\Omega(\sqrt{n})$ space is needed to obtain a better than $\alpha$ streaming approximation [ChouGV-20].
- For every $\varepsilon>0$, there is an $O(\log n)$ space $(4/9-\varepsilon)$-approximation linear sketching algorithm for MAX-DICUT; $\Omega(\sqrt{n})$ space is needed to obtain a better than $4/9$ streaming approximation [ChouGV-20].
- For every $\varepsilon>0$, there is an $O(\log n)$ space $(1/\sqrt{2}-\varepsilon)$-approximation linear sketching algorithm for MAX-$k$SAT; $\Omega(\sqrt{n})$ space is needed to obtain a better than $1/\sqrt{2}$ streaming approximation [ChouGV-20].
