TL;DR
This paper establishes stronger computational hardness results for lattice problems BDD and SVP in various $ extit{l}_p$ norms, based on assumptions related to the Gap-ETH hypothesis, indicating no efficient algorithms exist under these conjectures.
Contribution
It provides new fine-grained hardness bounds for lattice problems in all $ extit{l}_p$ norms, extending previous results and connecting them to Gap-(S)ETH assumptions.
Findings
No $2^{o(n)}$-time algorithms for certain BDD problems for all $p$ in $[1, \infty)$.
No $2^{o(n)}$-time algorithms for $ extit{l}_p$-SVP for large $p$, assuming Gap-SETH.
Explicit bounds on approximation factors and running times under various hypotheses.
Abstract
We show improved fine-grained hardness of two key lattice problems in the norm: Bounded Distance Decoding to within an factor of the minimum distance () and the (decisional) -approximate Shortest Vector Problem (), assuming variants of the Gap (Strong) Exponential Time Hypothesis (Gap-(S)ETH). Specifically, we show: 1. For all , there is no -time algorithm for for any constant , where and is the kissing-number constant, assuming and that non-uniform Gap-ETH holds. 2. For all , there is no -time algorithm for for any constant , where is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Improved Hardness of BDD and SVP Under Gap-(S)ETH· youtube
