首页> 外文期刊>Electronic Colloquium on Computational Complexity >Hardness Magnification for Natural Problems
【24h】

Hardness Magnification for Natural Problems

机译:自然问题的硬度放大

获取原文
           

摘要

We show that for several natural problems of interest, complexity lower bounds that are barely non-trivial imply super-polynomial or even exponential lower bounds in strong computational models. We term this phenomenon "hardness magnification". Our examples of hardness magnification include:1. Let MCSP [ s ] be the decision problem whose YES instances are truth tables of functions with circuit complexity at most s ( n ) . We show that if MCSP [ 2 n ] cannot be solved on average with zero error by formulas of linear (or even sub-linear) size, then NP does not have polynomial-size formulas. In contrast, Hirahara and Santhanam (2017) showed that MCSP [ 2 n ] cannot be solved in the worst case by formulas of nearly quadratic size.2. If there is a 0" c 0 such that for each positive integer d there is an 0" 0 such that the problem of checking if an n -vertex graph in the adjacency matrix representation has a vertex cover of size ( log n ) c cannot be solved by depth- d AC 0 circuits of size m 1+ , where m = ( n 2 ) , then NP does not have polynomial-size formulas.3. Let ( ) -MCSP [ s ] be the promise problem whose YES instances are truth tables of functions that are -approximable by a circuit of size s ( n ) , and whose NO instances are truth tables of functions that are not -approximable by a circuit of size s ( n ) . We show that for any non-trivial choice of the parameters and , if ( ) -MCSP [ 2 n ] cannot be solved by randomized algorithms with random access to the input running in sublinear time, then NP is not contained in BPP.4. If for each probabilistic quasi-linear time machine M using poly-logarithmic many random bits that is claimed to solve Satisfiability, there is a deterministic polynomial-time machine that on infinitely many input lengths n either identifies a satisfiable instance of bitlength n on which M does not accept with high probability or an unsatisfiable instance of bitlength n on which M does not reject with high probability, then NEXP is not contained in BPP.5. Given functions s c : N N where s c , let MKtP [ c s ] be the promise problem whose YES instances are strings of Kt complexity (Levin, 1984) at most c ( N ) and NO instances are strings of Kt complexity greater than s ( N ) . We show that if there is a 0" 0 such that for each 0" 0 , MKtP [ N N + 5 log ( N )] requires Boolean circuits of size N 1+ , then EXP is not contained in SIZE(poly).For each of the cases of magnification above, we observe that standard hardness assumptions imply much stronger lower bounds for these problems than we require for magnification.We further explore magnification as an avenue to proving strong lower bounds, and argue that magnification circumvents the "natural proofs" barrier of Razborov and Rudich (1997). Examining some standard proof techniques, we find that they fall just short of proving lower bounds via magnification. As one of our main open problems, we ask whether there are other meta-mathematical barriers to proving lower bounds that rule out approaches combining magnification with known techniques.
机译:我们表明,对于感兴趣的几个自然问题,在强大的计算模型中,复杂度较低的下限几乎不平凡就意味着超多项式甚至指数下限。我们称这种现象为“硬度放大”。我们的硬度放大倍数示例包括:1。令MCSP [s]为决策问题,其YES实例为电路复杂度最高为s(n)的函数真值表。我们表明,如果不能通过线性(甚至子线性)大小的公式来平均零误差地平均求解MCSP [2 n],那么NP就没有多项式大小的公式。相比之下,Hirahara和Santhanam(2017)指出,在最坏的情况下,MCSP [2 n]不能用近似二次大小的公式来求解。2。如果存在一个0“> c 0,从而对于每个正整数d,都存在一个0”> 0,使得检查邻接矩阵表示中的n-顶点图是否具有大小为(log n)的顶点覆盖的问题c不能用深度为m 1+的d AC 0电路求解,其中m =(n 2),则NP没有多项式大小公式。3。令()-MCSP [s]为应允问题,其YES实例是大小为s(n)的电路可逼近的函数真值表,而NO实例是a大小不能与-逼近的函数真值表。大小为s(n)的电路。我们表明,对于任何非平凡的参数选择,如果()-MCSP [2 n]不能通过随机算法求解,并且无法对在亚线性时间运行的输入进行随机访问,则NP不会包含在BPP.4中。如果对于使用多对数的许多随机比特要求解决可满足性的每个概率准线性时间机器M,则存在确定性多项式时间机器,该机器在无限多个输入长度n上标识一个可满足的比特长度n实例,在该实例上,M不能以很高的概率接受比特率n或M不能以高概率拒绝的不满意实例,那么BXP.5中不包含NEXP。给定函数sc:NN其中sc,让MKtP [cs]是一个承诺问题,其YES实例最多为Kt复杂度的字符串(Levin,1984),NO实例最多为s(N)的Kt复杂度的字符串。我们证明,如果存在一个0“ 0,使得对于每个0 0,MKtP [NN + 5 log(N)]需要大小为N 1+的布尔电路,则EXP不会包含在SIZE(poly)中。对于上述每种放大情况,我们观察到标准硬度假设所暗示的这些问题的下限比我们进行放大所需要的强得多。我们进一步探讨了放大率作为证明强下限的一种途径,并认为放大绕开了“自然”的界限。证明” Razborov和Rudich(1997)的障碍。通过检查一些标准的证明技术,我们发现它们不足以通过放大来证明下界。作为主要的开放性问题之一,我们询问是否存在其他超数学障碍来证明下界,从而排除了将放大倍数与已知技术相结合的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号