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It is well known that after placing n balls independently and uniformly at random into n bins, the fullest bin holds Θ(log n/ log log n) balls with high probability. More recently, Azar et al. analyzed the following process: randomly choose d bins for each ball, and then place the balls, one by one, into the least full bin from its d choices [2]. They show… (More)

We present a v ery simple, randomized approximation algorithm for determining the number of cliques in a random graph.

(MATH) We study approximation algorithms for the permanent of an <i>n</i> x <i>n</i> (0,1) matrix <i>A</i> based on the following simple idea: obtain a random matrix <i>B</i> by replacing each 1-entry of <i>A</i> independently by ± <i>e</i>, where <i>e</i> is a random basis element of a suitable algebra; then output |det(<i>B</i>)|<sup>2</sup>. This… (More)

It is well known that after placing n balls independently and uniformly at random into n bins, the fullest bin holds @(log n/ log log n) balls with high probability. Recently, Azar et al. analyzed the following: randomly choose d bins for each ball, and then sequentially place each ball in the least full of its chosen bins [2]. They show that the fullest… (More)

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