Making hard problems harder

Joshua Buresh-Oppenheim, R. Santhanam
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引用次数: 11

Abstract

We consider a general approach to the hoary problem of (im)proving circuit lower bounds. We define notions of hardness condensing and hardness extraction, in analogy to the corresponding notions from the computational theory of randomness. A hardness condenser is a procedure that takes in a Boolean function as input, as well as an advice string, and outputs a Boolean function on a smaller number of bits which has greater hardness when measured in terms of input length. A hardness extractor takes in a Boolean function as input, as well as an advice string, and outputs a Boolean function defined on a smaller number of bits which has close to maximum hardness. We prove several positive and negative results about these objects. First, we observe that hardness-based pseudo-random generators can be used to extract deterministic hardness from non-deterministic hardness. We derive several consequences of this observation. Among other results, we show that if E/O(n) has exponential non-deterministic hardness, then E/O{n) has deterministic hardness 2n/n, which is close to the maximum possible. We demonstrate a rare downward closure result: E with sub-exponential advice is contained in non-uniform space 2deltan for all delta > 0 if and only if there is k > 0 such that P with quadratic advice can be approximated in non-uniform space nk . Next, we consider limitations on natural models of hardness condensing and extraction. We show lower bounds on the advice length required for hardness condensing in a very general model of "relativizing" condensers. We show that non-trivial black-box extraction of deterministic hardness from deterministic hardness is essentially impossible. Finally, we prove positive results on hardness condensing in certain special cases. We show how to condense hardness from a biased function without advice using a hashing technique. We also give a hardness condenser without advice from average-case hardness to worst-case hardness. Our technique uses a connection between hardness condensing and explicit constructions of covering codes
让难题变得更难
我们考虑了证明电路下界这一棘手问题的一般方法。我们定义了硬度压缩和硬度提取的概念,类比于随机计算理论中的相应概念。硬度电容是一个过程,它接受一个布尔函数作为输入,以及一个建议字符串,并在较小的比特上输出一个布尔函数,当以输入长度衡量时,这个比特具有更大的硬度。硬度提取器接受一个布尔函数作为输入,以及一个建议字符串,并输出一个布尔函数,该函数定义在更小的比特数上,接近最大硬度。我们证明了关于这些对象的几个正负结果。首先,我们观察到基于硬度的伪随机生成器可以用于从非确定性硬度中提取确定性硬度。我们从这一观察得出了几个结论。结果表明,如果E/O(n)具有指数不确定性硬度,则E/O{n)具有确定性硬度2n/n,接近最大可能值。我们证明了一个罕见的向下闭包结果:对于所有> 0的函数,当且仅当k > 0,使得具有二次通知的P可以在非一致空间nk中近似时,具有次指数通知的E包含在非一致空间2delta中。接下来,我们考虑了硬度凝聚和萃取自然模型的局限性。我们在一个非常一般的“相对化”冷凝器模型中展示了硬度冷凝所需的建议长度的下界。我们证明了从确定性硬度中非平凡的黑盒提取确定性硬度本质上是不可能的。最后,在某些特殊情况下,我们证明了硬度凝聚的积极结果。我们将展示如何在没有建议的情况下使用哈希技术从有偏差的函数中压缩硬度。我们还给出了硬度表,没有从平均情况硬度到最坏情况硬度的建议。我们的技术使用了硬度压缩和覆盖代码的显式结构之间的联系
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