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29
Simple Extractors for All MinEntropies and a New PseudoRandom Generator
 Journal of the ACM
, 2001
"... A “randomness extractor ” is an algorithm that given a sample from a distribution with sufficiently high minentropy and a short random seed produces an output that is statistically indistinguishable from uniform. (Minentropy is a measure of the amount of randomness in a distribution). We present a ..."
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Cited by 107 (30 self)
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A “randomness extractor ” is an algorithm that given a sample from a distribution with sufficiently high minentropy and a short random seed produces an output that is statistically indistinguishable from uniform. (Minentropy is a measure of the amount of randomness in a distribution). We present a simple, selfcontained extractor construction that produces good extractors for all minentropies. Our construction is algebraic and builds on a new polynomialbased approach introduced by TaShma, Zuckerman, and Safra [TSZS01]. Using our improvements, we obtain, for example, an extractor with output length m = k/(log n) O(1/α) and seed length (1 + α) log n for an arbitrary 0 < α ≤ 1, where n is the input length, and k is the minentropy of the input distribution. A “pseudorandom generator ” is an algorithm that given a short random seed produces a long output that is computationally indistinguishable from uniform. Our technique also gives a new way to construct pseudorandom generators from functions that require large circuits. Our pseudorandom generator construction is not based on the NisanWigderson generator [NW94], and turns worstcase hardness directly into pseudorandomness. The parameters of our generator match those in [IW97, STV01] and in particular are strong enough to obtain a new proof that P = BP P if E requires exponential size circuits.
Extracting Randomness: A Survey and New Constructions
, 1999
"... this paper we do two things. First, we survey extractors and dispersers: what they are, how they can be designed, and some of their applications. The work described in the survey is due to a long list of research papers by various authors##most notably by David Zuckerman. Then, we present a new tool ..."
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Cited by 90 (5 self)
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this paper we do two things. First, we survey extractors and dispersers: what they are, how they can be designed, and some of their applications. The work described in the survey is due to a long list of research papers by various authors##most notably by David Zuckerman. Then, we present a new tool for constructing explicit extractors and give two new constructions that greatly improve upon previous results. The new tool we devise, a merger," is a function that accepts d strings, one of which is uniformly distributed and outputs a single string that is guaranteed to be uniformly distributed. We show how to build good explicit mergers, and how mergers can be used to build better extractors. Using this, we present two new constructions. The first construction succeeds in extracting all of the randomness from any somewhat random source. This improves upon previous extractors that extract only some of the randomness from somewhat random sources with enough" randomness. The amount of truly random bits used by this extractor, however, is not optimal. The second extractor we build extracts only some of the randomness and works only for sources with enough randomness, but uses a nearoptimal amount of truly random bits. Extractors and dispersers have many applications in removing randomness" in various settings and in making randomized constructions explicit. We survey some of these applications and note whenever our new constructions yield better results, e.g., plugging our new extractors into a previous construction we achieve the first explicit Nsuperconcentrators of linear size and polyloglog(N) depth. ] 1999 Academic Press CONTENTS 1.
Extractors and Pseudorandom Generators
 Journal of the ACM
, 1999
"... We introduce a new approach to constructing extractors. Extractors are algorithms that transform a "weakly random" distribution into an almost uniform distribution. Explicit constructions of extractors have a variety of important applications, and tend to be very difficult to obtain. ..."
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Cited by 87 (5 self)
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We introduce a new approach to constructing extractors. Extractors are algorithms that transform a "weakly random" distribution into an almost uniform distribution. Explicit constructions of extractors have a variety of important applications, and tend to be very difficult to obtain.
Derandomizing ArthurMerlin Games using Hitting Sets
, 1999
"... We prove that AM (and hence Graph Nonisomorphism) is in NP if for some > 0, some language in NE \ coNE requires nondeterministic circuits of size 2 n . This improves recent results of Arvind and K obler and of Klivans and Van Melkebeek who proved the same conclusion, but under stronger hardness a ..."
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Cited by 67 (0 self)
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We prove that AM (and hence Graph Nonisomorphism) is in NP if for some > 0, some language in NE \ coNE requires nondeterministic circuits of size 2 n . This improves recent results of Arvind and K obler and of Klivans and Van Melkebeek who proved the same conclusion, but under stronger hardness assumptions, namely, either the existence of a language in NE \ coNE which cannot be approximated by nondeterministic circuits of size less than 2 n or the existence of a language in NE \ coNE which requires oracle circuits of size 2 n with oracle gates for SAT (satisfiability). The previous results on derandomizing AM were based on pseudorandom generators. In contrast, our approach is based on a strengthening of Andreev, Clementi and Rolim's hitting set approach to derandomization. As a spinoff, we show that this approach is strong enough to give an easy (if the existence of explicit dispersers can be assumed known) proof of the following implication: For some > 0, if there is a l...
Circuit Minimization Problem
 In ACM Symposium on Theory of Computing (STOC
, 1999
"... We study the complexity of the circuit minimization problem: given the truth table of a Boolean function f and a parameter s, decide whether f can be realized by a Boolean circuit of size at most s. We argue why this problem is unlikely to be in P (or even in P=poly) by giving a number of surpris ..."
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Cited by 26 (1 self)
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We study the complexity of the circuit minimization problem: given the truth table of a Boolean function f and a parameter s, decide whether f can be realized by a Boolean circuit of size at most s. We argue why this problem is unlikely to be in P (or even in P=poly) by giving a number of surprising consequences of such an assumption. We also argue that proving this problem to be NPcomplete (if it is indeed true) would imply proving strong circuit lower bounds for the class E, which appears beyond the currently known techniques. Keywords: hard Boolean functions, derandomization, natural properties, NPcompleteness. 1 Introduction An nvariable Boolean function f n : f0; 1g n ! f0; 1g can be given by either its truth table of size 2 n , or a Boolean circuit whose size may be significantly smaller than 2 n . It is well known that most Boolean functions on n variables have circuit complexity at least 2 n =n [Sha49], but so far no family of sufficiently hard functions has ...
Constructions of NearOptimal Extractors Using PseudoRandom Generators
 Electronic Colloquium on Computational Complexity
, 1998
"... We introduce a new approach to construct extractors  combinatorial objects akin to expander graphs that have several applications. Our approach is based on error correcting codes and on the NisanWigderson pseudorandom generator. A straightforward application of our approach yields a construction ..."
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Cited by 20 (3 self)
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We introduce a new approach to construct extractors  combinatorial objects akin to expander graphs that have several applications. Our approach is based on error correcting codes and on the NisanWigderson pseudorandom generator. A straightforward application of our approach yields a construction that is simple to describe and analyze, does not use any of the standard techniques used in related results, and improves or subsumes almost all the previous constructions. 1 Introduction Informally defined, an extractor is a function that extracts randomness from a weakly random distribution. Explicit constructions of extractors have several applications and are typically very hard to achieve. In this paper we introduce a new approach to the explicit construction of extractors. Our approach yields a construction that improves most of the known results, and that is optimal for certain parameters. Furthermore, our construction is simple and uses techniques that were never used in this field...
Explicit ordispersers with polylogarithmic degree
 J. ACM
, 1998
"... An (N,M,T)ORdisperser is a bipartite multigraph G = (V,W,E) withV  = N, and W  = M, having the following expansion property: any subset of V having at least T vertices has a neighbor set of size at least M/2. For any pair of constants ξ,λ,1 ≥ ξ>λ ≥ 0, any sufficiently large N, andforany (log ..."
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Cited by 13 (1 self)
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An (N,M,T)ORdisperser is a bipartite multigraph G = (V,W,E) withV  = N, and W  = M, having the following expansion property: any subset of V having at least T vertices has a neighbor set of size at least M/2. For any pair of constants ξ,λ,1 ≥ ξ>λ ≥ 0, any sufficiently large N, andforany (log N)ξ (log N)λ T ≥ 2, M ≤ 2, we give an explicit elementary construction of an (N,M,T)ORdisperser such that the outdegree of any vertex in V is at most polylogarithmic in N. Using this with known applications of ORdispersers yields several results. First, our construction implies that the complexity class StrongRP defined by Sipser, equals RP. Second, for any fixed η>0, we give the first polynomialtime simulation of RP algorithms using the output of any “ηminimally random ” source. For any integral R>0, such a source accepts a single request for an Rbit string and generates the string according to a distribution that assigns probability at most 2−Rη to any string. It is minimally random in the sense that any weaker source is
Efficiently Approximable RealValued Functions
 Electronic Colloquium on Computational Complexity
, 2000
"... We consider a class, denoted APP, of realvalued functions f : f0; 1g n ! [0; 1] such that f can be approximated, to within any ffl ? 0, by a probabilistic Turing machine running in time poly(n; 1=ffl). We argue that APP can be viewed as a generalization of BPP, and show that APP contains a nat ..."
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Cited by 12 (2 self)
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We consider a class, denoted APP, of realvalued functions f : f0; 1g n ! [0; 1] such that f can be approximated, to within any ffl ? 0, by a probabilistic Turing machine running in time poly(n; 1=ffl). We argue that APP can be viewed as a generalization of BPP, and show that APP contains a natural complete problem: computing the acceptance probability of a given Boolean circuit; in contrast, no complete problems are known for BPP. We observe that all known complexitytheoretic assumptions under which BPP is easy (i.e., can be efficiently derandomized) imply that APP is easy; on the other hand, we show that BPP may be easy while APP is not, by constructing an appropriate oracle. 1 Introduction The complexity class BPP is traditionally considered a class of languages that can be efficiently decided with the help of randomness. While it does contain some natural problems, the "semantic" nature of its definition (on every input, a BPP machine must have either at least 3=4 or at...
Improved derandomization of BPP using a hitting set generator
 Proceedings of Random99, LNCS 1671
, 1999
"... A hittingset generator is a deterministic algorithm which generates a set of strings that intersects every dense set recognizable by a small circuit. A polynomial time hittingset generator readily implies RP = P . Andreev et. al. (ICALP'96, and JACM 1998) showed that if polynomialtime hitting ..."
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Cited by 12 (1 self)
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A hittingset generator is a deterministic algorithm which generates a set of strings that intersects every dense set recognizable by a small circuit. A polynomial time hittingset generator readily implies RP = P . Andreev et. al. (ICALP'96, and JACM 1998) showed that if polynomialtime hittingset generator in fact implies the much stronger conclusion BPP = P .
Onesided Versus Twosided Randomness
 In Proceedings of the 16th Symposium on Theoretical Aspects of Computer Science. Lecture Notes in Computer Science
, 1998
"... We demonstrate how to use Lautemann's proof that BPP is in \Sigma p 2 to exhibit that BPP is in RP PromiseRP . Immediate consequences show that if PromiseRP is easy or if there exist quick hitting set generators then P = BPP. Our proof vastly simplifies the proofs of the later result due to Andr ..."
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Cited by 11 (1 self)
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We demonstrate how to use Lautemann's proof that BPP is in \Sigma p 2 to exhibit that BPP is in RP PromiseRP . Immediate consequences show that if PromiseRP is easy or if there exist quick hitting set generators then P = BPP. Our proof vastly simplifies the proofs of the later result due to Andreev, Clementi and Rolim and Andreev, Clementi, Rolim and Trevisan. Clementi, Rolimand Trevisan question whether the promise is necessary for the above results, i.e. whether BPP ` RP RP for instance. We give a relativized world where P = RP 6= BPP and thus the promise is indeed needed. 1 Introduction Andreev, Clementi and Rolim [ACR98] show how given access to a quick hitting set generator, one can approximate the size of easily describable sets. As an immediate consequence one gets that if quick hitting set generators exist then P = BPP. Andreev, Clementi, Rolim and Trevisan [ACRT97] simplify the proof and apply the result to simulating BPP with weak random sources. Much earlier, Lautema...