Loss-less condensers, unbalanced expanders, and extractors

An extractor is a procedure which extracts randomness from a detective random source using a few additional random bits. Explicit extractor constructions have numerous applications and obtaining such constructions is an important derandomization goal. Trevisan recently introduced an elegant extractor construction, but the number of truly random bits required is suboptimal when the input source has low-min-entropy. Significant progress toward overcoming this bottleneck has been made, but so far has required complicated recursive techniques that lose the simplicity of Trevisan's construction. We give a clean method for overcoming this bottleneck by constructing {\em loss-less condensers}. which compress the n-bit input source without losing any min-entropy, using O(\log n) additional random bits. Our condensers are built using a simple modification of Trevisan's construction, and yield the best extractor constructions to date. Loss-less condensers also produce unbalanced bipartite expander graphs with small (polylogarithmic) degree D and very strong expansion of (1-\epilon)D. We give other applications of our construction, including dispersers with entropy loss O(\log n), depth two super-concentrators whose size is within a polylog of optimal, and an improved hardness of approximation result.

[1]  Noam Nisan,et al.  Hardness vs. randomness , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.

[2]  C. Umans Hardness of Approximating Minimization Problems , 1999, FOCS 1999.

[3]  Avi Wigderson,et al.  Near-optimal conversion of hardness into pseudo-randomness , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[4]  E. Szemerédi,et al.  Sorting inc logn parallel steps , 1983 .

[5]  Bruce M. Maggs,et al.  On-line algorithms for path selection in a nonblocking network , 1990, STOC '90.

[6]  Avi Wigderson,et al.  Extracting randomness via repeated condensing , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[7]  János Komlós,et al.  Deterministic simulation in LOGSPACE , 1987, STOC.

[8]  Nabil Kahale,et al.  Eigenvalues and expansion of regular graphs , 1995, JACM.

[9]  R. Impagliazzo,et al.  P=BPP unless E has sub-exponential circuits: Derandomizing the XOR Lemma , 2002 .

[10]  Luca Trevisan,et al.  Construction of extractors using pseudo-random generators (extended abstract) , 1999, STOC '99.

[11]  Nicholas Pippenger,et al.  Sorting and Selecting in Rounds , 1987, SIAM J. Comput..

[12]  Noam Nisan,et al.  Randomness is Linear in Space , 1996, J. Comput. Syst. Sci..

[13]  Christopher Umans,et al.  Hardness of approximating /spl Sigma//sub 2//sup p/ minimization problems , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[14]  Peter Bro Miltersen,et al.  Are bitvectors optimal? , 2000, STOC '00.

[15]  Avi Wigderson,et al.  Extractors and pseudo-random generators with optimal seed length , 2000, STOC '00.

[16]  Aravind Srinivasan,et al.  Explicit OR-dispersers with polylogarithmic degree , 1998, JACM.

[17]  Leslie G. Valiant,et al.  Graph-Theoretic Properties in computational Complexity , 1976, J. Comput. Syst. Sci..

[18]  David Zuckerman,et al.  General weak random sources , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[19]  Luca Trevisan,et al.  Pseudorandom generators without the XOR lemma , 1999, Proceedings. Fourteenth Annual IEEE Conference on Computational Complexity (Formerly: Structure in Complexity Theory Conference) (Cat.No.99CB36317).

[20]  Zvi Galil,et al.  Explicit Constructions of Linear-Sized Superconcentrators , 1981, J. Comput. Syst. Sci..

[21]  Bruce M. Maggs,et al.  On-Line Algorithms for Path Selection in a Nonblocking Network , 1996, SIAM J. Comput..

[22]  Ran Raz,et al.  Extracting all the randomness and reducing the error in Trevisan's extractors , 1999, STOC '99.

[23]  Amnon Ta-Shma,et al.  On extracting randomness from weak random sources (extended abstract) , 1996, STOC '96.

[24]  Miklos Santha,et al.  On Using Deterministic Functions to Reduce Randomness in Probabilistic Algorithms , 1987, Inf. Comput..

[25]  Leonid A. Levin,et al.  Security preserving amplification of hardness , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[26]  David Zuckerman Randomness-optimal oblivious sampling , 1997, Random Struct. Algorithms.

[27]  Paul Feldman,et al.  Wide-Sense Nonblocking Networks , 1988, SIAM J. Discret. Math..

[28]  Ran Raz,et al.  On recycling the randomness of states in space bounded computation , 1999, STOC '99.

[29]  Christopher Umans Pseudo-random generators for all hardnesses , 2002, STOC '02.

[30]  Michael Sipser,et al.  Expanders, Randomness, or Time versus Space , 1988, J. Comput. Syst. Sci..

[31]  Avi Wigderson,et al.  Expanders That Beat the Eigenvalue Bound: Explicit Construction and Applications , 1999, Comb..

[32]  Amnon Ta-Shma,et al.  On Extracting Randomness From Weak Random Sources , 1995, Electron. Colloquium Comput. Complex..

[33]  Noam Nisan,et al.  Extracting Randomness: A Survey and New Constructions , 1999, J. Comput. Syst. Sci..

[34]  D. Spielman,et al.  Expander codes , 1996 .

[35]  Omer Reingold,et al.  Randomness Conductors and Constant-Degree Expansion Beyond the Degree / 2 Barrier , 2001 .

[36]  Avi Wigderson,et al.  Extractors: optimal up to constant factors , 2003, STOC '03.

[37]  János Komlós,et al.  Sorting in c log n parallel sets , 1983, Comb..

[38]  Amnon Ta-Shma,et al.  Almost Optimal Dispersers , 1998, STOC '98.

[39]  Jaikumar Radhakrishnan,et al.  Bounds for Dispersers, Extractors, and Depth-Two Superconcentrators , 2000, SIAM J. Discret. Math..

[40]  C. Umans Hardness of Approximating p 2 Minimization Problems , 1999 .

[41]  Avi Wigderson,et al.  Randomness conductors and constant-degree lossless expanders , 2002, STOC '02.

[42]  Amnon Ta-Shma,et al.  Extractors from Reed-Muller Codes , 2001, Electron. Colloquium Comput. Complex..

[43]  Aravind Srinivasan,et al.  Computing with very weak random sources , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.