Journal of Computer and System Sciences
P = BPP if E requires exponential circuits: derandomizing the XOR lemma
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Resource-Bounded Kolmogorov Complexity Revisited
SIAM Journal on Computing
Graph Nonisomorphism Has Subexponential Size Proofs Unless the Polynomial-Time Hierarchy Collapses
SIAM Journal on Computing
New Bounds for the Language Compression Problem
COCO '00 Proceedings of the 15th Annual IEEE Conference on Computational Complexity
Extracting randomness from samplable distributions
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
A complexity theoretic approach to randomness
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Resource bounded symmetry of information revisited
Theoretical Computer Science - Mathematical foundations of computer science 2004
Language compression and pseudorandom generators
Computational Complexity
Low-Depth Witnesses are Easy to Find
CCC '07 Proceedings of the Twenty-Second Annual IEEE Conference on Computational Complexity
Worst-Case Running Times for Average-Case Algorithms
CCC '09 Proceedings of the 2009 24th Annual IEEE Conference on Computational Complexity
Symmetry of Information and Bounds on Nonuniform Randomness Extraction via Kolmogorov Extractors
CCC '11 Proceedings of the 2011 IEEE 26th Annual Conference on Computational Complexity
CSR'11 Proceedings of the 6th international conference on Computer science: theory and applications
Extracting kolmogorov complexity with applications to dimension zero-one laws
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I
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We show that if DTIME[2O(n)] is not included in DSPACE [2O(n)], then, for every set B in PSPACE, all strings x in B of length n can be represented by a string compressed(x) of length at most log(|B=n|) + O(log n), such that a polynomial-time algorithm, given compressed(x), can distinguish x from all the other strings in B=n. Modulo the O(log n) additive term, this achieves the information-theoretical optimum for string compression.