Algorithms - Sequential, Parallel - A Unified Appr. by R. Miller, L. Boxer

By R. Miller, L. Boxer

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We use C (x; r) to denote the output of C on input x and randomness r; thus C (x; r) = (C(x), r). The advantage of a randomized encoding is that et allows for a natural relaxation of condition (1): Instead of requiring that the mapping be injective, we can now consider encodings that are “almost injective” in the sense that given C (x; r), the encoding needs to be uniquely decodable only with high probability over r. In fact, we will further weaken this requirement substantially, and only require that C (x; r) be uniquely decodable with non-negligible probability.

Though for reasons more subtle than in the worst-case setting. Their argument yields search to decision connections even for interesting subclasses of distributional NP. For instance, if every language in NP is easy-on-average for decision algorithms with respect to the uniform distribution, then it is also easy-on-average for search algorithms with respect to the uniform distribution. 2. From a cryptographic perspective, the most important distributional search problem in NP is the problem of inverting a candidate one-way function.

If, on the other hand, Dn (x) > 2−|x| , let y be the string that precedes x in lexicographic order among the strings in {0, 1}n and let p = fDn (y) (if x is the empty string, then we let p = 0). Then we define C(x; n) = 1z. Here z is the longest common prefix of fDn (x) and p when both are written out in binary. Since fDn is computable in polynomial time, so is z. C is injective because only two binary strings s1 and s2 can have the same longest common prefix z; a third string s3 sharing z as a prefix must have a longer prefix with either s1 or s2 .

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