Compression of Samplable Sources
Luca Trevisan, Salil Vadhan, David Zuckerman · Computational Complexity · 2005
We study the compression of polynomially samplable sources. In particular, we give efficient prefix-free compression and decompression algorithms for three classes of such sources (whose support is a subset of {0, 1} n ). 1. We show how to compress sources X samplable by logspace machines to expected length H(X) + O(1). Our next results concern flat sources whose support is in P. 2. If H(X) ≤ k = n − O(log n), we show how to compress to expected length k + polylog(n − k). 3. If the support of X is the witness set for a self-reducible NP relation, then we show how to compress to expected length H(X) + 5.