On Exponential-time Hypotheses, Derandomization, and Circuit Lower Bounds

IF 2.3 2区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Journal of the ACM Pub Date : 2023-04-20 DOI:10.1145/3593581
Lijie Chen, Ron Rothblum, Roei Tell, Eylon Yogev
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引用次数: 2

Abstract

The Exponential-Time Hypothesis (ETH) is a strengthening of the 𝒫 ≠ 𝒩𝒫 conjecture, stating that 3- SAT on n variables cannot be solved in (uniform) time 2 εċ n , for some ε > 0. In recent years, analogous hypotheses that are “exponentially strong” forms of other classical complexity conjectures (such as 𝒩𝒫⊈ ℬ𝒫𝒫 or co 𝒩𝒫⊈𝒩𝒫) have also been introduced and have become widely influential. In this work, we focus on the interaction of exponential-time hypotheses with the fundamental and closely related questions of derandomization and circuit lower bounds . We show that even relatively mild variants of exponential-time hypotheses have far-reaching implications to derandomization, circuit lower bounds, and the connections between the two. Specifically, we prove that: (1) The Randomized Exponential-Time Hypothesis (rETH) implies that ℬ𝒫𝒫 can be simulated on “average-case” in deterministic (nearly-)polynomial-time (i.e., in time 2 Õ(log( n )) = n loglog( n ) O(1) ). The derandomization relies on a conditional construction of a pseudorandom generator with near-exponential stretch (i.e., with seed length Õ(log ( n ))); this significantly improves the state-of-the-art in uniform “hardness-to-randomness” results, which previously only yielded pseudorandom generators with sub-exponential stretch from such hypotheses. (2) The Non-Deterministic Exponential-Time Hypothesis (NETH) implies that derandomization of ℬ𝒫𝒫 is completely equivalent to circuit lower bounds against ℰ, and in particular that pseudorandom generators are necessary for derandomization. In fact, we show that the foregoing equivalence follows from a very weak version of NETH, and we also show that this very weak version is necessary to prove a slightly stronger conclusion that we deduce from it. Last, we show that disproving certain exponential-time hypotheses requires proving breakthrough circuit lower bounds. In particular, if CircuitSAT for circuits over n bits of size poly(n) can be solved by probabilistic algorithms in time 2 n /polylog(n) , then ℬ𝒫ℰ does not have circuits of quasilinear size.
关于指数时间假设、非随机化和回路下界
指数时间假设(ETH)是对(一致)时间2 ε >,对于某些ε >, n个变量上的3- SAT不能在(一致)时间2 ε >0. 近年来,类似的假设是其他经典复杂性猜想的“指数强”形式(如:或)也被引入并产生了广泛的影响。在这项工作中,我们关注指数时间假设与非随机化和电路下界的基本和密切相关的问题的相互作用。我们表明,即使是相对温和的指数时间假设变体,对非随机化、回路下界以及两者之间的联系也有深远的影响。具体地说,我们证明了:(1)随机指数时间假设(rETH)表明,在确定的(近)多项式时间(即时间2 Õ(log(n)) = n logog (n) O(1))中,可以对“平均情况”进行模拟。非随机化依赖于具有近指数延伸的伪随机生成器的条件构造(即种子长度Õ(log (n)));这显着提高了统一的“随机硬度”结果的最新技术,以前只能从这些假设中产生具有次指数拉伸的伪随机生成器。(2)非确定性指数时间假设(Non-Deterministic Exponential-Time Hypothesis, NETH)表明,任意化的条件完全等价于任意化的电路下界,特别是任意化需要伪随机发生器。事实上,我们证明了前面的等价是从一个非常弱的NETH版本推导出来的,我们还表明,这个非常弱的版本是证明我们从它推导出的一个稍微强一点的结论所必需的。最后,我们证明了反驳某些指数时间假设需要证明突破电路的下界。特别地,如果对于大小为poly(n)的n位以上电路的CircuitSAT可以用概率算法在2n /polylog(n)的时间内求解,则可以证明,任意的电路都不具有拟线性大小。
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来源期刊
Journal of the ACM
Journal of the ACM 工程技术-计算机:理论方法
CiteScore
7.50
自引率
0.00%
发文量
51
审稿时长
3 months
期刊介绍: The best indicator of the scope of the journal is provided by the areas covered by its Editorial Board. These areas change from time to time, as the field evolves. The following areas are currently covered by a member of the Editorial Board: Algorithms and Combinatorial Optimization; Algorithms and Data Structures; Algorithms, Combinatorial Optimization, and Games; Artificial Intelligence; Complexity Theory; Computational Biology; Computational Geometry; Computer Graphics and Computer Vision; Computer-Aided Verification; Cryptography and Security; Cyber-Physical, Embedded, and Real-Time Systems; Database Systems and Theory; Distributed Computing; Economics and Computation; Information Theory; Logic and Computation; Logic, Algorithms, and Complexity; Machine Learning and Computational Learning Theory; Networking; Parallel Computing and Architecture; Programming Languages; Quantum Computing; Randomized Algorithms and Probabilistic Analysis of Algorithms; Scientific Computing and High Performance Computing; Software Engineering; Web Algorithms and Data Mining
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