Abstract
Given a randomized algorithm A, a natural approach towards derandomizing it is to find a method for searching the associated sample space Ω for a good point ω with respect to a given input instance x; a point is good for x if A(x, ω) = f(x). Given such a point ω, the algorithm A(x,ω) becomes a deterministic algorithm and it is guaranteed to find the correct solution. The problem faced in searching the sample space is that it is usually exponential in size.
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© 2001 Springer-Verlag Berlin Heidelberg
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Jukna, S. (2001). Derandomization. In: Extremal Combinatorics. Texts in Theoretical Computer Science. An EATCS Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04650-0_28
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DOI: https://doi.org/10.1007/978-3-662-04650-0_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-08559-8
Online ISBN: 978-3-662-04650-0
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