163 research outputs found
Product Dimension of Forests and Bounded Treewidth Graphs
The product dimension of a graph G is defined as the minimum natural number l
such that G is an induced subgraph of a direct product of l complete graphs. In
this paper we study the product dimension of forests, bounded treewidth graphs
and k-degenerate graphs. We show that every forest on n vertices has a product
dimension at most 1.441logn+3. This improves the best known upper bound of
3logn for the same due to Poljak and Pultr. The technique used in arriving at
the above bound is extended and combined with a result on existence of
orthogonal Latin squares to show that every graph on n vertices with a
treewidth at most t has a product dimension at most (t+2)(logn+1). We also show
that every k-degenerate graph on n vertices has a product dimension at most
\ceil{8.317klogn}+1. This improves the upper bound of 32klogn for the same by
Eaton and Rodl.Comment: 12 pages, 3 figure
A Faster Parameterized Algorithm for Treedepth
The width measure \emph{treedepth}, also known as vertex ranking, centered
coloring and elimination tree height, is a well-established notion which has
recently seen a resurgence of interest. We present an algorithm which---given
as input an -vertex graph, a tree decomposition of the graph of width ,
and an integer ---decides Treedepth, i.e. whether the treedepth of the graph
is at most , in time . If necessary, a witness structure
for the treedepth can be constructed in the same running time. In conjunction
with previous results we provide a simple algorithm and a fast algorithm which
decide treedepth in time and ,
respectively, which do not require a tree decomposition as part of their input.
The former answers an open question posed by Ossona de Mendez and Nesetril as
to whether deciding Treedepth admits an algorithm with a linear running time
(for every fixed ) that does not rely on Courcelle's Theorem or other heavy
machinery. For chordal graphs we can prove a running time of for the same algorithm.Comment: An extended abstract was published in ICALP 2014, Track
Boxicity and separation dimension
A family of permutations of the vertices of a hypergraph is
called 'pairwise suitable' for if, for every pair of disjoint edges in ,
there exists a permutation in in which all the vertices in one
edge precede those in the other. The cardinality of a smallest such family of
permutations for is called the 'separation dimension' of and is denoted
by . Equivalently, is the smallest natural number so that
the vertices of can be embedded in such that any two
disjoint edges of can be separated by a hyperplane normal to one of the
axes. We show that the separation dimension of a hypergraph is equal to the
'boxicity' of the line graph of . This connection helps us in borrowing
results and techniques from the extensive literature on boxicity to study the
concept of separation dimension.Comment: This is the full version of a paper by the same name submitted to
WG-2014. Some results proved in this paper are also present in
arXiv:1212.6756. arXiv admin note: substantial text overlap with
arXiv:1212.675
Solving weighted and counting variants of connectivity problems parameterized by treewidth deterministically in single exponential time
It is well known that many local graph problems, like Vertex Cover and
Dominating Set, can be solved in 2^{O(tw)}|V|^{O(1)} time for graphs G=(V,E)
with a given tree decomposition of width tw. However, for nonlocal problems,
like the fundamental class of connectivity problems, for a long time we did not
know how to do this faster than tw^{O(tw)}|V|^{O(1)}. Recently, Cygan et al.
(FOCS 2011) presented Monte Carlo algorithms for a wide range of connectivity
problems running in time $c^{tw}|V|^{O(1)} for a small constant c, e.g., for
Hamiltonian Cycle and Steiner tree. Naturally, this raises the question whether
randomization is necessary to achieve this runtime; furthermore, it is
desirable to also solve counting and weighted versions (the latter without
incurring a pseudo-polynomial cost in terms of the weights).
We present two new approaches rooted in linear algebra, based on matrix rank
and determinants, which provide deterministic c^{tw}|V|^{O(1)} time algorithms,
also for weighted and counting versions. For example, in this time we can solve
the traveling salesman problem or count the number of Hamiltonian cycles. The
rank-based ideas provide a rather general approach for speeding up even
straightforward dynamic programming formulations by identifying "small" sets of
representative partial solutions; we focus on the case of expressing
connectivity via sets of partitions, but the essential ideas should have
further applications. The determinant-based approach uses the matrix tree
theorem for deriving closed formulas for counting versions of connectivity
problems; we show how to evaluate those formulas via dynamic programming.Comment: 36 page
On giant components and treewidth in the layers model
Given an undirected -vertex graph and an integer , let
denote the random vertex induced subgraph of generated by ordering
according to a random permutation and including in those
vertices with at most of their neighbors preceding them in this order.
The distribution of subgraphs sampled in this manner is called the \emph{layers
model with parameter} . The layers model has found applications in studying
-degenerate subgraphs, the design of algorithms for the maximum
independent set problem, and in bootstrap percolation.
In the current work we expand the study of structural properties of the
layers model.
We prove that there are -regular graphs for which with high
probability has a connected component of size . Moreover,
this connected component has treewidth . This lower bound on the
treewidth extends to many other random graph models. In contrast, is
known to be a forest (hence of treewidth~1), and we establish that if is of
bounded degree then with high probability the largest connected component in
is of size . We also consider the infinite two-dimensional
grid, for which we prove that the first four layers contain a unique infinite
connected component with probability
- β¦