## Abstract

For a family X of k-subsets of the set {1, ⋯, n}, let |X| be the cardinality of X and let Γ(X, μ) be the expected maximum weight of a subset from X when the weights of 1, ⋯, n are chosen independently at random from a symmetric probability distribution μ on ℝ. We consider the inverse isoperimetric problem of finding μ for which Γ(X, μ) gives the best estimate of ln |X|. We prove that the optimal choice of μ is the logistic distribution, in which case Γ(X, μ) provides an asymptotically tight estimate of ln |X| as k ^{-1} ln |X| grows. Since in many important cases Γ(X, μ) can be easily computed, we obtain computationally efficient approximation algorithms for a variety of counting problems. Given μ, we describe families X of a given cardinality with the minimum value of Γ(X, μ), thus extending and sharpening various isoperimetric inequalities in the Boolean cube.

Original language | English |
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Pages (from-to) | 159-191 |

Number of pages | 33 |

Journal | Israel Journal of Mathematics |

Volume | 158 |

DOIs | |

State | Published - Mar 2007 |

Externally published | Yes |

### Bibliographical note

Funding Information:* The research of the first author was partially supported by NSF Grants DMS 9734138 and DMS 0400617. ** The research of the second author was partially supported by ISF Grant 039-7165 and by GIF grant I-2052. Received May 24, 2005