4,437 research outputs found
Parallel Batch-Dynamic Graph Connectivity
In this paper, we study batch parallel algorithms for the dynamic
connectivity problem, a fundamental problem that has received considerable
attention in the sequential setting. The most well known sequential algorithm
for dynamic connectivity is the elegant level-set algorithm of Holm, de
Lichtenberg and Thorup (HDT), which achieves amortized time per
edge insertion or deletion, and time per query. We
design a parallel batch-dynamic connectivity algorithm that is work-efficient
with respect to the HDT algorithm for small batch sizes, and is asymptotically
faster when the average batch size is sufficiently large. Given a sequence of
batched updates, where is the average batch size of all deletions, our
algorithm achieves expected amortized work per
edge insertion and deletion and depth w.h.p. Our algorithm
answers a batch of connectivity queries in expected
work and depth w.h.p. To the best of our knowledge, our algorithm
is the first parallel batch-dynamic algorithm for connectivity.Comment: This is the full version of the paper appearing in the ACM Symposium
on Parallelism in Algorithms and Architectures (SPAA), 201
Faster Deterministic Fully-Dynamic Graph Connectivity
We give new deterministic bounds for fully-dynamic graph connectivity. Our
data structure supports updates (edge insertions/deletions) in
amortized time and connectivity queries in worst-case time, where is the number of vertices of the
graph. This improves the deterministic data structures of Holm, de Lichtenberg,
and Thorup (STOC 1998, J.ACM 2001) and Thorup (STOC 2000) which both have
amortized update time and worst-case query
time. Our model of computation is the same as that of Thorup, i.e., a pointer
machine with standard instructions.Comment: To appear at SODA 2013. 19 pages, 1 figur
Graph Connectivity in Noisy Sparse Subspace Clustering
Subspace clustering is the problem of clustering data points into a union of
low-dimensional linear/affine subspaces. It is the mathematical abstraction of
many important problems in computer vision, image processing and machine
learning. A line of recent work (4, 19, 24, 20) provided strong theoretical
guarantee for sparse subspace clustering (4), the state-of-the-art algorithm
for subspace clustering, on both noiseless and noisy data sets. It was shown
that under mild conditions, with high probability no two points from different
subspaces are clustered together. Such guarantee, however, is not sufficient
for the clustering to be correct, due to the notorious "graph connectivity
problem" (15). In this paper, we investigate the graph connectivity problem for
noisy sparse subspace clustering and show that a simple post-processing
procedure is capable of delivering consistent clustering under certain "general
position" or "restricted eigenvalue" assumptions. We also show that our
condition is almost tight with adversarial noise perturbation by constructing a
counter-example. These results provide the first exact clustering guarantee of
noisy SSC for subspaces of dimension greater then 3.Comment: 14 pages. To appear in The 19th International Conference on
Artificial Intelligence and Statistics, held at Cadiz, Spain in 201
Parallel Graph Connectivity in Log Diameter Rounds
We study graph connectivity problem in MPC model. On an undirected graph with
nodes and edges, round connectivity algorithms have been
known for over 35 years. However, no algorithms with better complexity bounds
were known. In this work, we give fully scalable, faster algorithms for the
connectivity problem, by parameterizing the time complexity as a function of
the diameter of the graph. Our main result is a
time connectivity algorithm for diameter- graphs, using total
memory. If our algorithm can use more memory, it can terminate in fewer rounds,
and there is no lower bound on the memory per processor.
We extend our results to related graph problems such as spanning forest,
finding a DFS sequence, exact/approximate minimum spanning forest, and
bottleneck spanning forest. We also show that achieving similar bounds for
reachability in directed graphs would imply faster boolean matrix
multiplication algorithms.
We introduce several new algorithmic ideas. We describe a general technique
called double exponential speed problem size reduction which roughly means that
if we can use total memory to reduce a problem from size to , for
in one phase, then we can solve the problem in
phases. In order to achieve this fast reduction for graph
connectivity, we use a multistep algorithm. One key step is a carefully
constructed truncated broadcasting scheme where each node broadcasts neighbor
sets to its neighbors in a way that limits the size of the resulting neighbor
sets. Another key step is random leader contraction, where we choose a smaller
set of leaders than many previous works do
Graph connectivity and universal rigidity of bar frameworks
Let be a graph on nodes. In this note, we prove that if is
-vertex connected, , then there exists a
configuration in general position in such that the bar framework
is universally rigid. The proof is constructive and is based on a
theorem by Lovasz et al concerning orthogonal representations and connectivity
of graphs [12,13].Comment: updated versio
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