## Abstract

Given metric spaces (X, d) and (Y, ρ) and an ordering x_{1}, x_{2}, . . ., x_{n} of (X, d), an embedding f : X → Y is said to have a prioritized distortion α(·), for a function α(·), if for any pair x_{j}, x^{0} of distinct points in X, the distortion provided by f for this pair is at most α(j). If Y is a normed space, the embedding is said to have prioritized dimension β(·), if f(x_{j}) may have at most β(j) nonzero coordinates. The notion of prioritized embedding was introduced by Filtser and the current authors in [EFN18], where a rather general methodology for constructing such embeddings was developed. Though this methodology enabled [EFN18] to come up with many prioritized embeddings, it typically incurs some loss in the distortion. In other words, in the worst-case, prioritized embeddings obtained via this methodology incur distortion which is at least a constant factor off, compared to the distortion of the classical counterparts of these embeddings. This constant loss is problematic for isometric embeddings. It is also troublesome for Matousek's embedding of general metrics into l_{∞}, which for a parameter k = 1, 2, . . ., provides distortion 2k−1 and dimension O(k log n·n^{1/k}). In this paper we devise two lossless prioritized embeddings. The first one is an isometric prioritized embedding of tree metrics into l_{∞} with dimension O(log j), matching the worst-case guarantee of O(log n) of the classical embedding of Linial et al. [LLR95]. The second one is a prioritized Matousek's embedding of general metrics into l_{∞}, which for a parameter k = 1, 2, . . ., provides prioritized distortion 2dk _{log}^{log}_{n}^{j} e− 1 and dimension O(k log n · n^{1/k}), again matching the worst-case guarantee 2k − 1 in the distortion of the classical Matousek's embedding. We also provide a dimension-prioritized variant of Matousek's embedding. Finally, we devise prioritized embeddings of general metrics into (single) ultra-metric and of general graphs into (single) spanning tree with asymptotically optimal distortion.

Original language | American English |
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Title of host publication | 31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020 |

Editors | Shuchi Chawla |

Pages | 1049-1062 |

Number of pages | 14 |

ISBN (Electronic) | 9781611975994 |

State | Published - 1 Jan 2020 |

Event | 31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020 - Salt Lake City, United States Duration: 5 Jan 2020 → 8 Jan 2020 |

### Publication series

Name | Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms |
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Volume | 2020-January |

### Conference

Conference | 31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020 |
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Country/Territory | United States |

City | Salt Lake City |

Period | 5/01/20 → 8/01/20 |

## All Science Journal Classification (ASJC) codes

- Software
- General Mathematics