Derandomization by Exploiting Redundancy and Mutual Independence

We present schemes for derandomizing parallel algorithms by exploiting redundancy of a shrinking sample space and the mutual independence of random variables. Our design uses n mutually independent random variables built on a sample space with exponential number of points. Our scheme yields an O(log n) time parallel algorithm for the PROFIT/COST problem using no more than linear number of processors.

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