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37
Simple Constructions of Almost kwise Independent Random Variables
, 1992
"... We present three alternative simple constructions of small probability spaces on n bits for which any k bits are almost independent. The number of bits used to specify a point in the sample space is (2 + o(1))(log log n + k/2 + log k + log 1 ɛ), where ɛ is the statistical difference between the dist ..."
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Cited by 270 (41 self)
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We present three alternative simple constructions of small probability spaces on n bits for which any k bits are almost independent. The number of bits used to specify a point in the sample space is (2 + o(1))(log log n + k/2 + log k + log 1 ɛ), where ɛ is the statistical difference between the distribution induced on any k bit locations and the uniform distribution. This is asymptotically comparable to the construction recently presented by Naor and Naor (our size bound is better as long as ɛ < 1/(k log n)). An additional advantage of our constructions is their simplicity.
SmallBias Probability Spaces: Efficient Constructions and Applications
 SIAM J. Comput
, 1993
"... We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are called fflbiased random variables. The number of random bits needed to generate the random variables is ..."
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Cited by 258 (15 self)
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We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are called fflbiased random variables. The number of random bits needed to generate the random variables is O(log n + log 1 ffl ). Thus, if ffl is polynomially small, then the size of the sample space is also polynomial. Random variables that are fflbiased can be used to construct "almost" kwise independent random variables where ffl is a function of k. These probability spaces have various applications: 1. Derandomization of algorithms: many randomized algorithms that require only k wise independence of their random bits (where k is bounded by O(log n)), can be derandomized by using fflbiased random variables. 2. Reducing the number of random bits required by certain randomized algorithms, e.g., verification of matrix multiplication. 3. Exhaustive testing of combinatorial circui...
How to Recycle Random Bits
, 1989
"... We show that modified versions of the linear congruential generator and the shift register generator are provably good for amplifying the correctness of a probabilistic algorithm. More precisely, if r random bits are needed for a BPP algorithm to be correct with probability at least 2=3, then O(r + ..."
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Cited by 183 (12 self)
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We show that modified versions of the linear congruential generator and the shift register generator are provably good for amplifying the correctness of a probabilistic algorithm. More precisely, if r random bits are needed for a BPP algorithm to be correct with probability at least 2=3, then O(r + k 2 ) bits are needed to improve this probability to 1 \Gamma 2 \Gammak . We also present a different pseudorandom generator that is optimal, up to a constant factor, in this regard: it uses only O(r + k) bits to improve the probability to 1 \Gamma 2 \Gammak . This generator is based on random walks on expanders. Our results do not depend on any unproven assumptions. Next we show that our modified versions of the shift register and linear congruential generators can be used to sample from distributions using, in the limit, the informationtheoretic lower bound on random bits. 1. Introduction Randomness plays a vital role in almost all areas of computer science, both in theory and in...
Simulating BPP Using a General Weak Random Source
 ALGORITHMICA
, 1996
"... We show how to simulate BPP and approximation algorithms in polynomial time using the output from a ffisource. A ffisource is a weak random source that is asked only once for R bits, and must output an Rbit string according to some distribution that places probability no more than 2 \GammaffiR on ..."
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Cited by 106 (19 self)
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We show how to simulate BPP and approximation algorithms in polynomial time using the output from a ffisource. A ffisource is a weak random source that is asked only once for R bits, and must output an Rbit string according to some distribution that places probability no more than 2 \GammaffiR on any particular string. We also give an application to the unapproximability of Max Clique.
ChernoffHoeffding Bounds for Applications with Limited Independence
 SIAM J. Discrete Math
, 1993
"... ChernoffHoeffding bounds are fundamental tools used in bounding the tail probabilities of the sums of bounded and independent random variables. We present a simple technique which gives slightly better bounds than these, and which more importantly requires only limited independence among the rando ..."
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Cited by 104 (10 self)
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ChernoffHoeffding bounds are fundamental tools used in bounding the tail probabilities of the sums of bounded and independent random variables. We present a simple technique which gives slightly better bounds than these, and which more importantly requires only limited independence among the random variables, thereby importing a variety of standard results to the case of limited independence for free. Additional methods are also presented, and the aggregate results are sharp and provide a better understanding of the proof techniques behind these bounds. They also yield improved bounds for various tail probability distributions and enable improved approximation algorithms for jobshop scheduling. The "limited independence" result implies that a reduced amount of randomness and weaker sources of randomness are sufficient for randomized algorithms whose analyses use the ChernoffHoeffding bounds, e.g., the analysis of randomized algorithms for random sampling and oblivious packet routi...
Dispersers, Deterministic Amplification, and Weak Random Sources.
, 1989
"... We use a certain type of expanding bipartite graphs, called disperser graphs, to design procedures for picking highly correlated samples from a finite set, with the property that the probability of hitting any sufficiently large subset is high. These procedures require a relatively small number of r ..."
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Cited by 93 (11 self)
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We use a certain type of expanding bipartite graphs, called disperser graphs, to design procedures for picking highly correlated samples from a finite set, with the property that the probability of hitting any sufficiently large subset is high. These procedures require a relatively small number of random bits and are robust with respect to the quality of the random bits. Using these sampling procedures to sample random inputs of polynomial time probabilistic algorithms, we can simulate the performance of some probabilistic algorithms with less random bits or with low quality random bits. We obtain the following results: 1. The error probability of an RP or BPP algorithm that operates with a constant error bound and requires n random bits, can be made exponentially small (i.e. 2 \Gamman ), with only (3 + ffl)n random bits, as opposed to standard amplification techniques that require \Omega\Gamma n 2 ) random bits for the same task. This result is nearly optimal, since the informati...
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.
Computing with Very Weak Random Sources
, 1994
"... For any fixed 6> 0, we show how to simulate RP algorithms in time nO(‘Ogn) using the output of a 6source wath minentropy R‘. Such a weak random source is asked once for R bits; it outputs an Rbit string such that any string has probability at most 2Rc. If 6> 1 l/(k + l), our BPP simulations tak ..."
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Cited by 73 (7 self)
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For any fixed 6> 0, we show how to simulate RP algorithms in time nO(‘Ogn) using the output of a 6source wath minentropy R‘. Such a weak random source is asked once for R bits; it outputs an Rbit string such that any string has probability at most 2Rc. If 6> 1 l/(k + l), our BPP simulations take time no(‘og(k)n) (log(k) is the logarithm iterated k times). We also gave a polynomialtime BPP simulation using ChorGoldreich sources of minentropy Ro(’), which is optimal. We present applications to timespace tradeoffs, expander constructions, and the hardness of approximation. Also of interest is our randomnessefficient Leflover Hash Lemma, found independently by Goldreich & Wigderson.
Weak Random Sources, Hitting Sets, and BPP Simulations
, 1998
"... We show how to simulate any BPP algorithm in polynomial time using a weak random source of r bits and minentropy r fl for any fl ? 0. This follows from a more general result about sampling with weak random sources. Our result matches an informationtheoretic lower bound and solves a question that ..."
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Cited by 40 (5 self)
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We show how to simulate any BPP algorithm in polynomial time using a weak random source of r bits and minentropy r fl for any fl ? 0. This follows from a more general result about sampling with weak random sources. Our result matches an informationtheoretic lower bound and solves a question that has been open for some years. The previous best results were a polynomial time simulation of RP [Saks, Srinivasan and Zhou 1995] and a quasipolynomial time simulation of BPP [TaShma 1996]. Departing significantly from previous related works, we do not use extractors; instead, we use the ORdisperser of [Saks, Srinivasan, and Zhou 1995] in combination with a tricky use of hitting sets borrowed from [Andreev, Clementi, and Rolim 1996]. AMS Subject Classification: 68Q10, 11K45. Key Words and Phrases: Derandomization, Imperfect Sources of Randomness, Hitting Sets, Randomized Computations, Expander Graphs. Abbreviated Title: BPP Simulations using Weak Random Sources. 1 Introduction Randomi...
On Deterministic Approximation of DNF
 In Proceedings of STOC'91
, 1993
"... We develop efficient deterministic algorithms for approximating the fraction of truth assignments that satisfy a disjunctive normal form formula. Although the algorithms themselves are deterministic, their analysis is probabilistic and uses the notion of limited independence between random variables ..."
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Cited by 23 (3 self)
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We develop efficient deterministic algorithms for approximating the fraction of truth assignments that satisfy a disjunctive normal form formula. Although the algorithms themselves are deterministic, their analysis is probabilistic and uses the notion of limited independence between random variables. International Computer Science Institute, 1947 Center Street, Berkeley, California 94704 and Computer Science Department, UC Berkeley, research partially supported by NSF operating grant CCR9016468 and by grant No. 8900312 from the United StatesIsrael Binational Science Foundation (BSF), Jerusalem, Israel. y Department of Mathematics, U.C. Berkeley, research partially supported by NSF, research partially done while visiting the International Computer Science Institute ii 1 Introduction Throughout this paper, let F denote a formula in disjunctive normal form (DNF) on n variables with m clauses of length at most t, and let Pr[F ] denote the probability that a random, independent and...