Foundations and Trends (R) in Theoretical Computer Science
1 total work
Pathwise Independence and Derandomization
by Michael Luby and Avi Wigderson
Published 22 August 2006
Pairwise Independence and Derandomization gives several applications of the following paradigm, which has proven extremely powerful in algorithm design and computational complexity. First, design a probabilistic algorithm for a given problem. Then, show that the correctness analysis of the algorithm remains valid even when the random strings used by the algorithm do not come from the uniform distribution, but rather from a small sample space, appropriately chosen. In some cases this can be proven directly (giving ""unconditional derandomization""), and in others it uses computational assumptions, like the existence of 1-way functions (giving ""conditional derandomization"").
The book is self contained, and is a prime manifestation of the ""derandomization"" paradigm. It is intended for scholars and graduate students in the field of theoretical computer science interested in randomness, derandomization and their interplay with computational complexity.
The book is self contained, and is a prime manifestation of the ""derandomization"" paradigm. It is intended for scholars and graduate students in the field of theoretical computer science interested in randomness, derandomization and their interplay with computational complexity.