Combinatorial cube packings in cube and torus
Mathieu Dutour Sikiri\'c, Yoshiaki Itoh

TL;DR
This paper investigates the properties of random cube packings in high-dimensional cubes and tori, introducing combinatorial packings to analyze their density and extensibility, revealing differences between cube and torus cases.
Contribution
It introduces the concept of combinatorial cube packings, derives density expansions, and characterizes extensibility and minimal non-extensible packings in high dimensions.
Findings
In the cube case, packings reduce to a single cube as N→∞.
In the torus case, non-extensible packings occur with positive probability for n≥3.
The number of parameters in torus packings is at least n(n+1)/2, conjectured to be at most 2^n-1.
Abstract
We consider sequential random packing of cubes with into the cube and the torus as . In the cube case as the random cube packings thus obtained are reduced to a single cube with probability . In the torus case the situation is different: for , sequential random cube packing yields cube tilings, but for with strictly positive probability, one obtains non-extensible cube packings. So, we introduce the notion of combinatorial cube packing, which instead of depending on depend on some parameters. We use use them to derive an expansion of the packing density in powers of . The explicit computation is done in the cube case. In the torus case, the situation is more complicate and we restrict ourselves to the case of…
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
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptimization and Packing Problems · graph theory and CDMA systems · Digital Image Processing Techniques
