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arXiv 2004.09238cs.CCcs.DM
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有界度图上近似平均的复杂性

The complexity of approximating averages on bounded-degree graphs

Andreas Galanis, Daniel Stefankovic, Eric Vigoda

摘要:

我们证明了,除非P=NP,有没有多项式时间算法近似在一些乘法常数的平均大小的独立集的最大程度6图。这是一个特殊的情况下,一个更一般的结果,为硬核模型定义的独立集加权参数$λ>0$。在一般设置中,我们证明,除非P=NP,所有$Δ\geq 3$,所有$λ>λ_c(Δ)$,有没有FPTAS适用于所有的最大程度$Δ$图计算的平均大小的独立集的吉布斯分布,其中$λ_c(Δ)$是临界点的唯一性/非唯一性相变的$Δ$ -正则树。此外,我们证明了$λ$在这个非唯一性区域的稠密集的问题是NP-困难的一些常数因子内近似。我们的工作扩展到反铁磁伊辛模型,并推广到所有的2-自旋反铁磁模型,建立计算的平均磁化强度在树的非唯一性区域的难度。此前,Schulman,Sinclair和Srivastava(2015)表明,精确计算平均磁化强度是#P-hard,但没有近似结果的硬度。Sly(2010)和Sly and Sun(2014)近似配分函数的困难结果并不意味着计算平均值的困难。在我们的减少新的成分是一个复杂的结构对根树的边际分布在根同意,但他们的衍生物不同意。主要的技术贡献是控制什么边际分布和导数是可实现的,并使用柯西函数方程来论证小工具的存在。

英文摘要:

We prove that, unless P=NP, there is no polynomial-time algorithm to approximate within some multiplicative constant the average size of an independent set in graphs of maximum degree 6. This is a special case of a more general result for the hard-core model defined on independent sets weighted by a parameter $λ>0$. In the general setting, we prove that, unless P=NP, for all $Δ\geq 3$, all $λ>λ_c(Δ)$, there is no FPTAS which applies to all graphs of maximum degree $Δ$ for computing the average size of the independent set in the Gibbs distribution, where $λ_c(Δ)$ is the critical point for the uniqueness/non-uniqueness phase transition on the $Δ$-regular tree. Moreover, we prove that for $λ$ in a dense set of this non-uniqueness region the problem is NP-hard to approximate within some constant factor. Our work extends to the antiferromagnetic Ising model and generalizes to all 2-spin antiferromagnetic models, establishing hardness of computing the average magnetization in the tree non-uniqueness region. Previously, Schulman, Sinclair and Srivastava (2015) showed that it is #P-hard to compute the average magnetization exactly, but no hardness of approximation results were known. Hardness results of Sly (2010) and Sly and Sun (2014) for approximating the partition function do not imply hardness of computing averages. The new ingredient in our reduction is an intricate construction of pairs of rooted trees whose marginal distributions at the root agree but their derivatives disagree. The main technical contribution is controlling what marginal distributions and derivatives are achievable and using Cauchy's functional equation to argue existence of the gadgets.

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