Abstract
This paper studies distributions which can be sampled by randomized algorithms in time polynomial in the length of their outputs. Those distributions are called “polynomial-time samplable” and important to average-case complexity theory, cryptography, and statistical physics. This paper shows that those distributions are exactly as hard as #P-functions to compute deterministically and at least as hard as NP-sets to approximate by deterministic protocols.
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Yamakami, T. (1996). Polynomial time samplable distributions. In: Penczek, W., Szałas, A. (eds) Mathematical Foundations of Computer Science 1996. MFCS 1996. Lecture Notes in Computer Science, vol 1113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61550-4_179
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DOI: https://doi.org/10.1007/3-540-61550-4_179
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