Average case complete problems
SIAM Journal on Computing
On the theory of average case complexity
Journal of Computer and System Sciences
A Pseudorandom Generator from any One-way Function
SIAM Journal on Computing
Foundations and Trends® in Theoretical Computer Science
No better ways to generate hard NP instances than picking uniformly at random
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
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When we represent a decision problem, like CIRCUIT-SAT, as a language over the binary alphabet, we usually do not specify how to encode instances by binary strings. This relies on the empirical observation that the truth of a statement of the form "CIRCUIT-SAT belongs to a complexity class C" does not depend on the encoding, provided both the encoding and the class C are "natural". In this sense most of the Complexity theory is "encoding invariant".The notion of a polynomial time computable distribution from Average Case Complexity is one of the exceptions from this rule. It might happen that a distribution over some objects, like circuits, is polynomial time computable in one encoding and is not polynomial time computable in the other encoding. In this paper we suggest an encoding invariant generalization of a notion of a polynomial time computable distribution. The completeness proofs of known distributional problems, like Bounded Halting, are simpler for the new class than for polynomial time computable distributions.