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Summary: Uniform Derandomization from Pathetic Lower Bounds
Eric Allender
Department of Computer Science
Rutgers University
New Brunswick, NJ 08855, USA
allender@cs.rutgers.edu
V Arvind
The Institute of Mathematical Sciences
C.I.T. Campus
Chennai 600 113, India
arvind@imsc.res.in
Fengming Wang
Department of Computer Science
Rutgers University
New Brunswick, NJ, 08855 USA
fengming@cs.rutgers.edu
July 15, 2010
Abstract
A recurring theme in the literature on derandomization is that probabilistic algorithms can be simulated
quickly by deterministic algorithms, if one can obtain impressive (i.e., superpolynomial, or even nearly-exponential)
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