## Abstract

We prove that the integrality gap of the Goemans-Linial semi-definite programming relaxation for the Sparsest Cut Problem is Ω(√logn) on inputs with n vertices, thus matching the previously best known upper bound (log n)^{1/2+o(1)} up to lower-order factors. This statement is a consequence of the following new isoperimetric-type inequality. Consider the 8-regular graph whose vertex set is the 5-dimensional integer grid ℤ^{5} and where each vertex (a, b, c, d, e) ∈ ℤ^{5} is connected to the 8 vertices (a ± 1, b, c, d, e), (a, b ± 1, c, d, e), (a, b, c ± 1, d, e ± a), (a, b, c, d ± 1, e ± b). This graph is known as the Cayley graph of the 5-dimensional discrete Heisenberg group. Given Ω ⊆ ℤ^{5}, denote the size of its edge boundary in this graph (a.k.a. the horizontal perimeter of Ω) by |∂_{h}Ω|. For t ∈ N, denote by |∂^{t}_{v}Ω| the number of (a, b, c, d, e) ∈ ℤ^{5} such that exactly one of the two vectors (a, b, c, d, e), (a, b, c, d, e + f) is in ω. The vertical perimeter of Ω is defined to be |∂_{v}Ω| = √Σ^{∞}_{t=1} |∂^{t}_{v}Ω|^{2}/t^{2}. We show that every subset Ω ⊆ ℤ^{5} satisfies |∂_{v}Ω| = O(|∂_{h}Ω|). This vertical-versus-horizontal isoperimetric inequality yields the above-stated integrality gap for Sparsest Cut and answers several geometric and analytic questions of independent interest. The theorem stated above is the culmination of a program whose aim is to understand the performance of the Goemans-Linial semi-definite program through the embeddability properties of Heisenberg groups. These investigations have mathematical significance even beyond their established relevance to approximation algorithms and combinatorial optimization. In particular they contribute to a range of mathematical disciplines including functional analysis, geometric group theory, harmonic analysis, sub-Riemannian geometry, geometric measure theory, ergodic theory, group representations, and metric differentiation. This article builds on the above cited works, with the "twist" that while those works were equally valid for any finite dimensional Heisenberg group, our result holds for the Heisenberg group of dimension 5 (or higher) but fails for the 3-dimensional Heisenberg group. This insight leads to our core contribution, which is a deduction of an endpoint L1-boundedness of a certain singular integral on ℝ^{5} from the (local) L_{2}-boundedness of the corresponding singular integral on ℝ^{3}. To do this, we devise a corona-type decomposition of subsets of a Heisenberg group, in the spirit of the construction that David and Semmes performed in ℝ^{n}, but with two main conceptual differences (in addition to more technical differences that arise from the peculiarities of the geometry of Heisenberg group). Firstly, the "atoms" of our decomposition are perturbations of intrinsic Lipschitz graphs in the sense of Franchi, Serapioni, and Serra Cassano (plus the requisite "wild" regions that satisfy a Carleson packing condition). Secondly, we control the local overlap of our corona decomposition by using quantitative monotonicity rather than Jones-type β-numbers.

Original language | English (US) |
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Title of host publication | STOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing |

Editors | Pierre McKenzie, Valerie King, Hamed Hatami |

Publisher | Association for Computing Machinery |

Pages | 564-575 |

Number of pages | 12 |

ISBN (Electronic) | 9781450345286 |

DOIs | |

State | Published - Jun 19 2017 |

Event | 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017 - Montreal, Canada Duration: Jun 19 2017 → Jun 23 2017 |

### Publication series

Name | Proceedings of the Annual ACM Symposium on Theory of Computing |
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Volume | Part F128415 |

ISSN (Print) | 0737-8017 |

### Other

Other | 49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017 |
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Country/Territory | Canada |

City | Montreal |

Period | 6/19/17 → 6/23/17 |

## All Science Journal Classification (ASJC) codes

- Software

## Keywords

- Approximation algorithms
- Metric embeddings
- Semidefinite programming
- Sparsest cut problem