7573
2020
eng
525
547
11(1)
article
0
--
--
--
Time-Space Trade-Offs for Computing Euclidean Minimum Spanning Trees
We present time-space trade-offs for computing the Euclidean minimum spanning tree of a set S of n point-sites in the plane. More precisely, we assume that S resides in a random-access memory that can only be read. The edges of the Euclidean minimum spanning tree EMST(S) have to be reported sequentially, and they cannot be accessed or modified afterwards. There is a parameter s in {1, ..., n} so that the algorithm may use O(s) cells of read-write memory (called the workspace) for its computations. Our goal is to find an algorithm that has the best possible running time for any given s between 1 and n.
We show how to compute EMST(S) in O(((n^3)/(s^2)) log s) time with O(s) cells of workspace, giving a smooth trade-off between the two best-known bounds O(n^3) for s = 1 and O(n log n) for s = n. For this, we run Kruskal's algorithm on the "relative neighborhood graph" (RNG) of S. It is a classic fact that the minimum spanning tree of RNG(S) is exactly EMST(S). To implement Kruskal's algorithm with O(s)
cells of workspace, we define s-nets, a compact representation of planar graphs. This allows us to efficiently maintain and update the components of the current minimum spanning forest as the edges are being inserted.
Journal of Computational Geometry (JoCG)
https://jocg.org/index.php/jocg/article/view/473
yes
2020-10-21
Bahareh Banyassady
Bahareh Banyassady
Luis Barba
Wolfgang Mulzer
Visual Data Analysis
Visual Data Analysis in Science and Engineering
no-project
Visual and Data-centric Computing