AI paper index
Improved lower bounds for the Shannon capacity of odd cycles
One-line summary
An AI research paper on Improved lower bounds for the Shannon capacity of odd cycles.
Engineering notes
Engineering notes will be added by the aipentium editorial team.
Chinese explanation / 中文解读
中文解读待补充:本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。
Original abstract
The Shannon capacity $Θ(G)$ of a graph $G$ quantifies the maximum rate at which information can be transmitted with zero error over a noisy channel. It is lower bounded by $α(G^d)^{1/d}$ for any $d$, where $α(G^d)$ is the independence number of the $d$-th strong power of $G$. We construct independent sets of size $134753$ in $C_7^{10}$, $21909$ in $C_{11}^{6}$, and $62530$ in $C_{13}^{6}$, improving the best known lower bounds for the Shannon capacity of these graphs to $Θ(C_7)\geq 134753^{1/10}>3.258020$, $Θ(C_{11})\geq 21909^{1/6}>5.289773$, and $Θ(C_{13})\geq 62530^{1/6}>6.300109$. We also improve the best known lower bounds on the independence numbers of several individual strong powers of odd cycles that do not improve the Shannon capacity lower bound. The constructions were discovered through iterative interactions with a Large Language Model (LLM), illustrating the potential of LLMs for finding explicit combinatorial constructions.
Links and sources
Need this topic turned into a technical roadmap?
aipentium can prepare a custom AI literature review, code map, dataset map, and B2B technology assessment.
Request B2B AI research
Comments