7 research outputs found
Low-Memory Algorithms for Online and W-Streaming Edge Coloring
For edge coloring, the online and the W-streaming models seem somewhat
orthogonal: the former needs edges to be assigned colors immediately after
insertion, typically without any space restrictions, while the latter limits
memory to sublinear in the input size but allows an edge's color to be
announced any time after its insertion. We aim for the best of both worlds by
designing small-space online algorithms for edge-coloring. We study the problem
under both (adversarial) edge arrivals and vertex arrivals. Our results
significantly improve upon the memory used by prior online algorithms while
achieving an -competitive ratio. In particular, for -node graphs with
maximum vertex-degree under edge arrivals, we obtain an online
-coloring in space. This is also the
first W-streaming edge-coloring algorithm for -coloring in sublinear
memory. All prior works either used linear memory or colors.
We also achieve a smooth color-space tradeoff: for any , we get an
-coloring in space,
improving upon the state of the art that used space for
the same number of colors. The improvements stem from extensive use of random
permutations that enable us to avoid previously used colors. Most of our
algorithms can be derandomized and extended to multigraphs, where edge coloring
is known to be considerably harder than for simple graphs.Comment: 32 pages, 1 figur
On-line Edge-Coloring with a Fixed Number of Colors
We investigate a variant of on-line edge-coloring in which there is a fixed number of colors available and the aim is to color as many edges as possible. We prove upper and lower bounds on the performance of different classes of algorithms for the problem. Furthermore, we determine the performance of two specific algorithms, First-Fit and Next-Fit
On-Line Edge-Coloring with a Fixed Number of Colors
We investigate a variant of on-line edge-coloring in which there is a fixed number of colors available and the aim is to color as many edges as possible. We prove upper and lower bounds on the performance of different classes of algorithms for the problem. Moreover, we determine..