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19th Annual IEEE Conference on Computational Complexity (CCC'04)
Compression of Samplable Sources
Amherst, Massachusetts
June 21-June 24
ISBN: 0-7695-2120-7
Luca Trevisan, University of California at Berkeley
Salil Vadhan, Harvard University
David Zuckerman, University of Texas
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 length k + δ ⋅ (n - k) for any constant ?δ > 0; in quasi-polynomial time we show how to compress to length k + O(polylog log(n - k)) even if k = n - polylog(n).
  • 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) + 4.
  • Citation:
    Luca Trevisan, Salil Vadhan, David Zuckerman, "Compression of Samplable Sources," ccc, pp.1-14, 19th Annual IEEE Conference on Computational Complexity (CCC'04), 2004
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