Polynomially Low Error PCPs with polyloglog n Queries via Modular Composition

Irit Dinur, P. Harsha, Guy Kindler
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引用次数: 15

Abstract

We show that every language in NP has a PCP verifier that tosses O(log n) random coins, has perfect completeness, and a soundness error of at most 1/poly(n), while making O(poly log log n) queries into a proof over an alphabet of size at most n1/poly log log n. Previous constructions that obtain 1/poly(n) soundness error used either poly log n queries or an exponential alphabet, i.e. of size 2nc for some c> 0. Our result is an exponential improvement in both parameters simultaneously. Our result can be phrased as polynomial-gap hardness for approximate CSPs with arity poly log log n and alphabet size n1/poly log n. The ultimate goal, in this direction, would be to prove polynomial hardness for CSPs with constant arity and polynomial alphabet size (aka the sliding scale conjecture for inverse polynomial soundness error). Our construction is based on a modular generalization of previous PCP constructions in this parameter regime, which involves a composition theorem that uses an extra 'consistency' query but maintains the inverse polynomial relation between the soundness error and the alphabet size. Our main technical/conceptual contribution is a new notion of soundness, which we refer to as distributional soundness, that replaces the previous notion of "list decoding soundness", and allows us to invoke composition a super-constant number of times without incurring a blow-up in the soundness error.
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基于模组合的多对数n查询多项式低误差pcp
我们证明了NP中的每种语言都有一个PCP验证器,该验证器投掷O(log n)个随机硬币,具有完美的完备性,并且稳健性误差最多为1/poly(n),而在最大n1/poly log log n的字母表上进行O(poly log log n)查询以证明。先前获得1/poly(n)稳健性误差的构造使用poly log n查询或指数字母表,即对于某些c> 0的大小为2nc。我们的结果是两个参数同时呈指数级提高。我们的结果可以被描述为多项式间隙硬度近似的csp,其密度为poly log log n,字母大小为n1/poly log n。在这个方向上,最终目标是证明具有恒定密度和多项式字母大小的csp的多项式硬度(又名逆多项式稳稳性误差的滑动尺度猜想)。我们的构造是基于该参数体系中先前的PCP构造的模块化推广,其中涉及一个组合定理,该定理使用了额外的“一致性”查询,但保持了稳健性误差和字母大小之间的逆多项式关系。我们的主要技术/概念贡献是一个新的稳健性概念,我们将其称为分布稳健性,它取代了之前的“列表解码稳健性”概念,并允许我们调用组合的超常数次数而不会导致稳健性错误的爆发。
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