We prove lower bounds for proofs of the bit pigeonhole principle (BPHP) and its generalizations in bounded-depth resolution over parities (Res$(\oplus)$). For weak BPHP$_n^m$ with $m = cn$ pigeons (for any constant $c>1$) and $n$ holes, for all $ε>0$, we prove that any depth $N^{1.5 - ε}$ proof in Res$(\oplus)$ must have exponential size, where $N = cn\log n$ is the number of variables. Inspired by recent work in TFNP on multicollision-finding, we consider a generalization of the bit pigeonhole principle, denoted $t$-BPHP$_n^m$, asserting that there is a map from $[m]$ to $[n]$ ($m > (t-1)n$) such that each $i \in [n]$ has fewer than $t$ preimages. We prove that any depth $N^{2-1/t-ε}$ proof in Res$(\oplus)$ of $t$-BPHP$_n^{ctn}$ (for any constant $c \geq 1$) must have exponential size. For the usual bit pigeonhole principle, we show that any depth $N^{2-ε}$ Res$(\oplus)$ proof of BPHP$_n^{n+1}$ must have exponential size. As a byproduct of our proof, we obtain that any randomized parity decision tree for the collision-finding problem with $n+1$ pigeons and $n$ holes must have depth $Ω(n)$, which matches the upper bound coming from a deterministic decision tree. We also prove a lifting theorem for bounded-depth Res$(\oplus)$ with a constant size gadget which lifts from $(p, q)$-DT-hardness, recently defined by Bhattacharya and Chattopadhyay. By combining our lifting theorem with the $(Ω(n), Ω(n))$-DT-hardness of the $n$-variate Tseitin contradiction over a suitable expander, proved by Bhattacharya and Chattopadhyay, we obtain an $N$-variate constant-width unsatisfiable CNF formula with $O(N)$ clauses for which any depth $N^{2-ε}$ Res$(\oplus)$ proof requires size $\exp(Ω(N^ε))$. Previously no superpolynomial lower bounds were known for Res$(\oplus)$ proofs when the depth is superlinear in the size of the formula.
翻译:我们证明了在基于奇偶性的有界深度分解(Res$(\\oplus)$)中,比特鸽巢原理(BPHP)及其推广形式的证明下界。对于弱BPHP$_n^m$,其中$m = cn$只鸽子(对于任意常数$c>1$)和$n$个洞,对于所有$ε>0$,我们证明任何深度为$N^{1.5 - ε}$的Res$(\\oplus)$证明必须具有指数级规模,其中$N = cn\\log n$是变量数。受近期TFNP中关于多重碰撞发现研究的启发,我们考虑比特鸽巢原理的一个推广形式,记为$t$-BPHP$_n^m$,它断言存在一个从$[m]$到$[n]$的映射($m > (t-1)n$),使得每个$i \\in [n]$的原像少于$t$个。我们证明,对于$t$-BPHP$_n^{ctn}$(对于任意常数$c \\geq 1$),任何深度为$N^{2-1/t-ε}$的Res$(\\oplus)$证明必须具有指数级规模。对于通常的比特鸽巢原理,我们证明任何深度为$N^{2-ε}$的Res$(\\oplus)$证明对于BPHP$_n^{n+1}$必须具有指数级规模。作为我们证明的副产品,我们得到:对于具有$n+1$只鸽子和$n$个洞的碰撞发现问题,任何随机奇偶决策树的深度必须为$Ω(n)$,这与确定性决策树的上界相匹配。我们还证明了有界深度Res$(\\oplus)$的一个提升定理,该定理使用常数大小的构件,从$(p, q)$-DT-硬度(由Bhattacharya和Chattopadhyay最近定义)进行提升。通过将我们的提升定理与Bhattacharya和Chattopadhyay证明的、在适当扩展图上的$n$变量Tseitin矛盾的$(Ω(n), Ω(n))$-DT-硬度相结合,我们得到一个具有$O(N)$个子句的$N$变量常数宽度不可满足CNF公式,对于该公式,任何深度为$N^{2-ε}$的Res$(\\oplus)$证明需要规模$\\exp(Ω(N^ε))$。此前,当证明深度超线性于公式规模时,Res$(\\oplus)$证明的超多项式下界尚属未知。