Alexander E. Andreev, Andrea E. F. Clementi, Jose' D. P. Rolim

We show that hitting sets can derandomize any BPP-algorithm.

This gives a positive answer to a fundamental open question in

probabilistic algorithms. More precisely, we present a polynomial

time deterministic algorithm which uses any given hitting set

to approximate the fractions of 1's in the ...
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Johannes Köbler, Rainer Schuler

We use the assumption that all sets in NP (or other levels

of the polynomial-time hierarchy) have efficient average-case

algorithms to derive collapse consequences for MA, AM, and various

subclasses of P/poly. As a further consequence we show for

C in {P(PP), PSPACE} that ...
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Valentine Kabanets, Charles Rackoff, Stephen Cook

We consider a class, denoted APP, of real-valued functions

f:{0,1}^n\rightarrow [0,1] such that f can be approximated, to

within any epsilon>0, by a probabilistic Turing machine running in

time poly(n,1/epsilon). We argue that APP can be viewed as a

generalization of BPP, and show that APP contains a natural

complete ...
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