Under the auspices of the Computational Complexity Foundation (CCF)

REPORTS > DETAIL:

### Revision(s):

Revision #1 to TR19-164 | 21st May 2020 12:48

#### Improved Bounds for Perfect Sampling of $k$-Colorings in Graphs

Revision #1
Authors: Siddharth Bhandari, Sayantan Chakraborty
Accepted on: 21st May 2020 12:49
Downloads: 90
Keywords:

Abstract:

We present a randomized algorithm that takes as input an undirected $n$-vertex graph $G$ with maximum degree $\Delta$ and an integer $k > 3\Delta$, and returns a random proper $k$-coloring of $G$. The
distribution of the coloring is \emph{perfectly} uniform over the set of all proper $k$-colorings; the expected running time of the algorithm is $\mathrm{poly}(k,n)=\widetilde{O}(n\Delta^2\cdot \log(k))$.
This improves upon a result of Huber~(STOC 1998) who obtained a polynomial time perfect sampling algorithm for $k>\Delta^2+2\Delta$.
Prior to our work, no algorithm with expected running time $\mathrm{poly}(k,n)$ was known to guarantee perfectly sampling with sub-quadratic number of colors in general.

Our algorithm (like several other perfect sampling algorithms including Huber's) is based on the Coupling from the Past method. Inspired by the \emph{bounding chain} approach, pioneered independently by
Huber~(STOC 1998) and
H\"aggstr\"om \& Nelander~(Scand.{} J.{} Statist., 1999), we employ a novel bounding chain to derive our result for the graph coloring problem.

Changes to previous version:

Rewrite of previous version.

### Paper:

TR19-164 | 6th November 2019 11:48

#### Improved bounds for perfect sampling of $k$-colorings in graphs

TR19-164
Authors: Siddharth Bhandari, Sayantan Chakraborty
Publication: 17th November 2019 12:56
Downloads: 331
Keywords:

Abstract:

We present a randomized algorithm that takes as input an undirected $n$-vertex graph $G$ with maximum degree $\Delta$ and an integer $k > 3\Delta$, and returns a random proper $k$-coloring of $G$. The
distribution of the coloring is perfectly uniform over the set of all proper $k$-colorings; the expected running time of the algorithm is $\mathrm{poly}(k,n)=\widetilde{O}(n\Delta^2\cdot \log(k))$.
This improves upon a result of Huber~(STOC 1998) who obtained polynomial time perfect sampling algorithm for $k>\Delta^2+2\Delta$.
Prior to our work, no algorithm with expected running time $\mathrm{poly}(k,n)$ was known to guarantee perfectly sampling for $\Delta = \omega(1)$ and for any $k \leq \Delta^2+2\Delta$.

Our algorithm (like several other perfect sampling algorithms including Huber's) is based on the Coupling from the Past method. Inspired by the bounding chain approach pioneered independently by H\"aggstr\"om \& Nelander~(Scand.{} J.{} Statist., 1999) and Huber~(STOC 1998), our algorithm is based on a novel bounding chain for the coloring problem.

ISSN 1433-8092 | Imprint