On Disperser/Lifting Properties of the Index and Inner-Product Functions

P. Beame, Sajin Koroth
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引用次数: 2

Abstract

Query-to-communication lifting theorems, which connect the query complexity of a Boolean function to the communication complexity of an associated `lifted' function obtained by composing the function with many copies of another function known as a gadget, have been instrumental in resolving many open questions in computational complexity. Several important complexity questions could be resolved if we could make substantial improvements in the input size required for lifting with the Index function, from its current near-linear size down to polylogarithmic in the number of inputs $N$ of the original function or, ideally, constant. The near-linear size bound was shown by Lovett, Meka, Mertz, Pitassi and Zhang using a recent breakthrough improvement on the Sunflower Lemma to show that a certain graph associated with the Index function of near-linear size is a disperser. They also stated a conjecture about the Index function that is essential for further improvements in the size required for lifting with Index using current techniques. In this paper we prove the following; 1) The conjecture of Lovett et al. is false when the size of the Index gadget is $\log N-\omega(1)$. 2) Also, the Inner-Product function, which satisfies the disperser property at size $O(\log N)$, does not have this property when its size is $\log N-\omega(1)$. 3) Nonetheless, using Index gadgets of size at least 4, we prove a lifting theorem for a restricted class of communication protocols in which one of the players is limited to sending parities of its inputs. 4) Using the ideas from this lifting theorem, we derive a strong lifting theorem from decision tree size to parity decision tree size. We use this to derive a general lifting theorem in proof complexity from tree-resolution size to tree-like $Res(\oplus)$ refutation size, which yields many new exponential lower bounds on such proofs.
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指数函数和内积函数的分散/提升性质
查询-通信提升定理(query -to-communication lifting theorem)将一个布尔函数的查询复杂度与一个关联的“提升”函数的通信复杂度联系起来,该“提升”函数是通过将该函数与另一个称为小工具的函数的许多副本组合而成的,它有助于解决计算复杂性中的许多悬而未决的问题。如果我们能够对Index函数提升所需的输入大小进行实质性改进,将其从当前的近线性大小降低到原始函数的输入数量$N$的多对数大小,或者理想情况下是常数,那么几个重要的复杂性问题就可以得到解决。Lovett, Meka, Mertz, Pitassi和Zhang利用最近对向日葵引证的突破性改进证明了近线性大小的索引函数相关的某个图是分散器。他们还提出了一个关于Index函数的猜想,该猜想对于使用当前技术进一步改进Index提升所需的大小至关重要。本文证明了以下几点:1)当Index小部件的大小为$\log N-\omega(1)$时,Lovett等人的猜想为假。2)同样,在尺寸为$O(\log N)$时满足分散剂性质的内积函数在尺寸为$\log N-\omega(1)$时不具有该性质。3)尽管如此,使用大小至少为4的Index小工具,我们证明了限制通信协议类别的提升定理,其中一个参与者仅限于发送其输入的配对。4)利用该提升定理的思想,导出了从决策树大小到奇偶决策树大小的强提升定理。我们利用这一点推导出了证明复杂度从树分辨率大小到树状$Res(\oplus)$反驳大小的一般提升定理,该定理给出了许多新的指数下界。
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