A set of vertices in a hypergraph is called an independent set if no hyperedge is completely contained inside the set. Given a hypergraph, computing its largest size independent set is an NP-hard problem. In this work, we study the independent set problem on hypergraphs in a natural semi-random family of instances. Our semi-random model is inspired by the Feige-Kilian model [FK01]. This popular model has also been studied in the works of [FK01, Ste17, MMT20] etc. McKenzie, Mehta, and Trevisan [MMT20] gave algorithms for computing independent sets in such a semi-random family of graphs. The algorithms by McKenzie et al. [MMT20] are based on rounding a "crude-SDP". We generalize their results and techniques to hypergraphs for an analogous family of hypergraph instances. Our algorithms are based on rounding the "crude-SDP" of McKenzie et al. [MMT20], augmented with "Lasserre/SoS like" hierarchy of constraints. Analogous to the results of McKenzie et al. [MMT20], we study the ranges of input parameters where we can recover the planted independent set or a large independent set.
翻译:高光谱中的一组脊椎是一个独立设置, 如果没有完全包含在集中的话, 则称为独立设置。 在高光谱中, 计算其最大大小的独立设置是一个NP- 硬问题 。 在这项工作中, 我们研究自然半随机环境体系中的高光谱问题 。 我们的半随机模型受Feige- Kilian 模型[ FK01] 的启发。 这个流行模型也在[ FK01, Ste17, MMT20] 等[ FKK01, Ste17, MMMMT20] 的作品中研究过。 McKenzie, Mehta, 和 Trevisan [MMMMT20] 的作品中, 给出了在这种半随机图组中独立计算数的算法。 由 McKenzie et al. [MMMT20] 的算法基于一个“ Lasserre/Sososocial ” 等独立参数, 和“ 我们的磁系” 的大规模输入序列序列。 我们的磁制成。 我们的磁系统和磁系的系统系统系统系统系统的系统可以回收结果。