  We are interested in computing the treewidth $\tw(G)$ of a given graph $G$.
Our approach is to design heuristic algorithms for computing a sequence of
improving upper bounds and a sequence of improving lower bounds, which would
hopefully converge to $\tw(G)$ from both sides. The upper bound algorithm
extends and simplifies Tamaki's unpublished work on a heuristic use of the
dynamic programming algorithm for deciding treewidth due to Bouchitt\'{e} and
Todinca. The lower bound algorithm is based on the well-known fact that, for
every minor $H$ of $G$, we have $\tw(H) \leq \tw(G)$. Starting from a greedily
computed minor $H_0$ of $G$, the algorithm tries to construct a sequence of
minors $H_0$, $H_1$, \ldots $H_k$ with $\tw(H_i) < \tw(H_{i + 1})$ for $0 \leq
i < k$ and hopefully $\tw(H_k) = \tw(G)$.
  We have implemented a treewidth solver based on this approach and have
evaluated it on the bonus instances from the exact treewidth track of PACE 2017
algorithm implementation challenge. The results show that our approach is
extremely effective in tackling instances that are hard for conventional
solvers. Our solver has an additional advantage over conventional ones in that
it attaches a compact certificate to the lower bound it computes.

Data Structures and Algorithms