2,021 research outputs found
Sampling Arborescences in Parallel
We study the problem of sampling a uniformly random directed rooted spanning tree, also known as an arborescence, from a possibly weighted directed graph. Classically, this problem has long been known to be polynomial-time solvable; the exact number of arborescences can be computed by a determinant [Tutte, 1948], and sampling can be reduced to counting [Jerrum et al., 1986; Jerrum and Sinclair, 1996]. However, the classic reduction from sampling to counting seems to be inherently sequential. This raises the question of designing efficient parallel algorithms for sampling. We show that sampling arborescences can be done in RNC.
For several well-studied combinatorial structures, counting can be reduced to the computation of a determinant, which is known to be in NC [Csanky, 1975]. These include arborescences, planar graph perfect matchings, Eulerian tours in digraphs, and determinantal point processes. However, not much is known about efficient parallel sampling of these structures. Our work is a step towards resolving this mystery
Computing and counting longest paths on circular-arc graphs in polynomial time.
The longest path problem asks for a path with the largest number of vertices in a given graph. The first polynomial time algorithm (with running time O(n4)) has been recently developed for interval graphs. Even though interval and circular-arc graphs look superficially similar, they differ substantially, as circular-arc graphs are not perfect. In this paper, we prove that for every path P of a circular-arc graph G, we can appropriately âcutâ the circle, such that the obtained (not induced) interval subgraph GⲠof G admits a path PⲠon the same vertices as P. This non-trivial result is of independent interest, as it suggests a generic reduction of a number of path problems on circular-arc graphs to the case of interval graphs with a multiplicative linear time overhead of O(n). As an application of this reduction, we present the first polynomial algorithm for the longest path problem on circular-arc graphs, which turns out to have the same running time O(n4) with the one on interval graphs, as we manage to get rid of the linear overhead of the reduction. This algorithm computes in the same time an n-approximation of the number of different vertex sets that provide a longest path; in the case where G is an interval graph, we compute the exact number. Moreover, our algorithm can be directly extended with the same running time to the case where every vertex has an arbitrary positive weight
Approximate Counting via Correlation Decay on Planar Graphs
We show for a broad class of counting problems, correlation decay (strong
spatial mixing) implies FPTAS on planar graphs. The framework for the counting
problems considered by us is the Holant problems with arbitrary constant-size
domain and symmetric constraint functions. We define a notion of regularity on
the constraint functions, which covers a wide range of natural and important
counting problems, including all multi-state spin systems, counting graph
homomorphisms, counting weighted matchings or perfect matchings, the subgraphs
world problem transformed from the ferromagnetic Ising model, and all counting
CSPs and Holant problems with symmetric constraint functions of constant arity.
The core of our algorithm is a fixed-parameter tractable algorithm which
computes the exact values of the Holant problems with regular constraint
functions on graphs of bounded treewidth. By utilizing the locally tree-like
property of apex-minor-free families of graphs, the parameterized exact
algorithm implies an FPTAS for the Holant problem on these graph families
whenever the Gibbs measure defined by the problem exhibits strong spatial
mixing. We further extend the recursive coupling technique to Holant problems
and establish strong spatial mixing for the ferromagnetic Potts model and the
subgraphs world problem. As consequences, we have new deterministic
approximation algorithms on planar graphs and all apex-minor-free graphs for
several counting problems
Counting and sampling problems on Eulerian graphs
In this thesis we consider two sets of combinatorial structures defined on an Eulerian
graph: the Eulerian orientations and Euler tours. We are interested in the computational
problems of counting (computing the number of elements in the set) and sampling
(generating a random element of the set). Specifically, we are interested in the question
of when there exists an efficient algorithm for counting or sampling the elements of
either set.
The Eulerian orientations of a number of classes of planar lattices are of practical
significance as they correspond to configurations of certain models studied in statistical
physics. In 1992 Mihail and Winkler showed that counting Eulerian orientations of a
general Eulerian graph is #P-complete and demonstrated that the problem of sampling
an Eulerian orientation can be reduced to the tractable problem of sampling a perfect
matching of a bipartite graph. We present a proof that this problem remains #Pcomplete
when the input is restricted to being a planar graph, and analyse a natural
algorithm for generating random Eulerian orientations of one of the afore-mentioned
planar lattices. Moreover, we make some progress towards classifying the range of
planar graphs on which this algorithm is rapidly mixing by exhibiting an infinite class
of planar graphs for which the algorithm will always take an exponential amount of
time to converge.
The problem of counting the Euler tours of undirected graphs has proven to be less
amenable to analysis than that of Eulerian orientations. Although it has been known
for many years that the number of Euler tours of any directed graph can be computed in
polynomial time, until recently very little was known about the complexity of counting
Euler tours of an undirected graph. Brightwell and Winkler showed that this problem is
#P-complete in 2005 and, apart from a few very simple examples, e.g., series-parellel
graphs, there are no known tractable cases, nor are there any good reasons to believe
the problem to be intractable. Moreover, despite several unsuccessful attempts, there
has been no progress made on the question of approximability. Indeed, this problem
was considered to be one of the more difficult open problems in approximate counting
since long before the complexity of exact counting was resolved. By considering a
randomised input model, we are able to show that a very simple algorithm can sample
or approximately count the Euler tours of almost every d-in/d-out directed graph in
expected polynomial time. Then, we present some partial results towards showing that
this algorithm can be used to sample or approximately count the Euler tours of almost
every 2d-regular graph in expected polynomial time. We also provide some empirical
evidence to support the unproven conjecture required to obtain this result. As a sideresult
of this work, we obtain an asymptotic characterisation of the distribution of the
number of Eulerian orientations of a random 2d-regular graph
Dynamic Connectivity in Disk Graphs
Let S â R2 be a set of n sites in the plane, so that every site s â S has an associated
radius rs > 0. Let D(S) be the disk intersection graph defined by S, i.e., the graph
with vertex set S and an edge between two distinct sites s, t â S if and only if the
disks with centers s, t and radii rs , rt intersect. Our goal is to design data structures
that maintain the connectivity structure of D(S) as sites are inserted and/or deleted
in S. First, we consider unit disk graphs, i.e., we fix rs = 1, for all sites s â S.
For this case, we describe a data structure that has O(log2 n) amortized update time
and O(log n/ log log n) query time. Second, we look at disk graphs with bounded
radius ratio Ψ, i.e., for all s â S, we have 1 ⤠rs ⤠Ψ, for a parameter Ψ that is
known in advance. Here, we not only investigate the fully dynamic case, but also the
incremental and the decremental scenario, where only insertions or only deletions of
sites are allowed. In the fully dynamic case, we achieve amortized expected update
time O(Ψ log4 n) and query time O(log n/ log log n). This improves the currently
best update time by a factor of Ψ. In the incremental case, we achieve logarithmic
dependency on Ψ, with a data structure that has O(ι(n)) amortized query time and
O(log Ψ log4 n) amortized expected update time, where ι(n) denotes the inverse Ackermann
function. For the decremental setting, we first develop an efficient decremental
disk revealing data structure: given two sets R and B of disks in the plane, we can delete
disks from B, and upon each deletion, we receive a list of all disks in R that no longer
intersect the union of B. Using this data structure, we get decremental data structures
with a query time of O(log n/ log log n) that supports deletions in O(n log Ψ log4 n)
overall expected time for disk graphs with bounded radius ratio Ψ and O(n log5 n)
overall expected time for disk graphs with arbitrary radii, assuming that the deletion
sequence is oblivious of the internal random choices of the data structures
Fast counting with tensor networks
We introduce tensor network contraction algorithms for counting satisfying
assignments of constraint satisfaction problems (#CSPs). We represent each
arbitrary #CSP formula as a tensor network, whose full contraction yields the
number of satisfying assignments of that formula, and use graph theoretical
methods to determine favorable orders of contraction. We employ our heuristics
for the solution of #P-hard counting boolean satisfiability (#SAT) problems,
namely monotone #1-in-3SAT and #Cubic-Vertex-Cover, and find that they
outperform state-of-the-art solvers by a significant margin.Comment: v2: added results for monotone #1-in-3SAT; published versio
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