### Abstract

We initiate the study of the computational complexity of the covering radius problem for point lattices, and approximation versions of the problem for both lattices and linear codes. We also investigate the computational complexity of the shortest linearly independent vectors problem, and its relation to the covering radius problem for lattices. For the covering radius on n-dimensional lattices, we show that the problem can be approximated within any constant factor γ(n) > 1 in random exponential time 2 ^{O(n)}, it is in AM for γ(n) = 2, in coAM for γ(n) = √n/ log n, and in NP ∩ coMP for γ(n) = √n. For the covering radius on n-dimensional linear codes, we show that the problem can be solved in deterministic polynomial time for approximation factor γ(n) = log n, but cannot be solved in polynomial time for some γ(n) = Ω(log log n) unless MP can be simulated in deterministic n ^{O(log log log n)} time. Moreover, we prove that the problem is NP-hard for every constant approximation factor, it is II _{2}-hard for some constant approximation factor, and it is in AM for approximation factor 2. So, it is unlikely to be II _{2}-hard for approximation factors larger than 2. This is a natural hardness of approximation result in the polynomial hierarchy. For the shortest independent vectors problem, we give a coAM protocol achieving approximation factor γ(n) = √n/ log n, solving an open problem of Blömer and Seifert (STOC'99), and prove that the problem is also in coNP for γ(n) = √n. Both results are obtained by giving a gap-preserving non-deterministic polynomial time reduction to the closest vector problem.

Original language | English (US) |
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Title of host publication | Proceedings of the Annual IEEE Conference on Computational Complexity |

Pages | 161-173 |

Number of pages | 13 |

Volume | 19 |

State | Published - 2004 |

Event | Proceedings - 19th IEEE Annual Conference on Computational Complexity - Amherst, MA, United States Duration: Jun 21 2004 → Jun 24 2004 |

### Other

Other | Proceedings - 19th IEEE Annual Conference on Computational Complexity |
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Country | United States |

City | Amherst, MA |

Period | 6/21/04 → 6/24/04 |

### Fingerprint

### ASJC Scopus subject areas

- Computational Mathematics

### Cite this

*Proceedings of the Annual IEEE Conference on Computational Complexity*(Vol. 19, pp. 161-173)

**The complexity of the covering radius problem on lattices and codes.** / Guruswami, Venkatesan; Micciancio, Daniele; Regev, Oded.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the Annual IEEE Conference on Computational Complexity.*vol. 19, pp. 161-173, Proceedings - 19th IEEE Annual Conference on Computational Complexity, Amherst, MA, United States, 6/21/04.

}

TY - GEN

T1 - The complexity of the covering radius problem on lattices and codes

AU - Guruswami, Venkatesan

AU - Micciancio, Daniele

AU - Regev, Oded

PY - 2004

Y1 - 2004

N2 - We initiate the study of the computational complexity of the covering radius problem for point lattices, and approximation versions of the problem for both lattices and linear codes. We also investigate the computational complexity of the shortest linearly independent vectors problem, and its relation to the covering radius problem for lattices. For the covering radius on n-dimensional lattices, we show that the problem can be approximated within any constant factor γ(n) > 1 in random exponential time 2 O(n), it is in AM for γ(n) = 2, in coAM for γ(n) = √n/ log n, and in NP ∩ coMP for γ(n) = √n. For the covering radius on n-dimensional linear codes, we show that the problem can be solved in deterministic polynomial time for approximation factor γ(n) = log n, but cannot be solved in polynomial time for some γ(n) = Ω(log log n) unless MP can be simulated in deterministic n O(log log log n) time. Moreover, we prove that the problem is NP-hard for every constant approximation factor, it is II 2-hard for some constant approximation factor, and it is in AM for approximation factor 2. So, it is unlikely to be II 2-hard for approximation factors larger than 2. This is a natural hardness of approximation result in the polynomial hierarchy. For the shortest independent vectors problem, we give a coAM protocol achieving approximation factor γ(n) = √n/ log n, solving an open problem of Blömer and Seifert (STOC'99), and prove that the problem is also in coNP for γ(n) = √n. Both results are obtained by giving a gap-preserving non-deterministic polynomial time reduction to the closest vector problem.

AB - We initiate the study of the computational complexity of the covering radius problem for point lattices, and approximation versions of the problem for both lattices and linear codes. We also investigate the computational complexity of the shortest linearly independent vectors problem, and its relation to the covering radius problem for lattices. For the covering radius on n-dimensional lattices, we show that the problem can be approximated within any constant factor γ(n) > 1 in random exponential time 2 O(n), it is in AM for γ(n) = 2, in coAM for γ(n) = √n/ log n, and in NP ∩ coMP for γ(n) = √n. For the covering radius on n-dimensional linear codes, we show that the problem can be solved in deterministic polynomial time for approximation factor γ(n) = log n, but cannot be solved in polynomial time for some γ(n) = Ω(log log n) unless MP can be simulated in deterministic n O(log log log n) time. Moreover, we prove that the problem is NP-hard for every constant approximation factor, it is II 2-hard for some constant approximation factor, and it is in AM for approximation factor 2. So, it is unlikely to be II 2-hard for approximation factors larger than 2. This is a natural hardness of approximation result in the polynomial hierarchy. For the shortest independent vectors problem, we give a coAM protocol achieving approximation factor γ(n) = √n/ log n, solving an open problem of Blömer and Seifert (STOC'99), and prove that the problem is also in coNP for γ(n) = √n. Both results are obtained by giving a gap-preserving non-deterministic polynomial time reduction to the closest vector problem.

UR - http://www.scopus.com/inward/record.url?scp=4944234593&partnerID=8YFLogxK

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M3 - Conference contribution

VL - 19

SP - 161

EP - 173

BT - Proceedings of the Annual IEEE Conference on Computational Complexity

ER -