42,232 research outputs found
Non-adaptive Group Testing on Graphs
Grebinski and Kucherov (1998) and Alon et al. (2004-2005) study the problem
of learning a hidden graph for some especial cases, such as hamiltonian cycle,
cliques, stars, and matchings. This problem is motivated by problems in
chemical reactions, molecular biology and genome sequencing.
In this paper, we present a generalization of this problem. Precisely, we
consider a graph G and a subgraph H of G and we assume that G contains exactly
one defective subgraph isomorphic to H. The goal is to find the defective
subgraph by testing whether an induced subgraph contains an edge of the
defective subgraph, with the minimum number of tests. We present an upper bound
for the number of tests to find the defective subgraph by using the symmetric
and high probability variation of Lov\'asz Local Lemma
-Stars or On Extending a Drawing of a Connected Subgraph
We consider the problem of extending the drawing of a subgraph of a given
plane graph to a drawing of the entire graph using straight-line and polyline
edges. We define the notion of star complexity of a polygon and show that a
drawing of an induced connected subgraph can be extended with at
most bends per edge, where is the
largest star complexity of a face of and is the size of the
largest face of . This result significantly improves the previously known
upper bound of [5] for the case where is connected. We also show
that our bound is worst case optimal up to a small additive constant.
Additionally, we provide an indication of complexity of the problem of testing
whether a star-shaped inner face can be extended to a straight-line drawing of
the graph; this is in contrast to the fact that the same problem is solvable in
linear time for the case of star-shaped outer face [9] and convex inner face
[13].Comment: Appears in the Proceedings of the 26th International Symposium on
Graph Drawing and Network Visualization (GD 2018
New Results on Quantum Property Testing
We present several new examples of speed-ups obtainable by quantum algorithms
in the context of property testing. First, motivated by sampling algorithms, we
consider probability distributions given in the form of an oracle
. Here the probability \PP_f(j) of an outcome is the
fraction of its domain that maps to . We give quantum algorithms for
testing whether two such distributions are identical or -far in
-norm. Recently, Bravyi, Hassidim, and Harrow \cite{BHH10} showed that if
\PP_f and \PP_g are both unknown (i.e., given by oracles and ), then
this testing can be done in roughly quantum queries to the
functions. We consider the case where the second distribution is known, and
show that testing can be done with roughly quantum queries, which we
prove to be essentially optimal. In contrast, it is known that classical
testing algorithms need about queries in the unknown-unknown case and
about queries in the known-unknown case. Based on this result, we
also reduce the query complexity of graph isomorphism testers with quantum
oracle access. While those examples provide polynomial quantum speed-ups, our
third example gives a much larger improvement (constant quantum queries vs
polynomial classical queries) for the problem of testing periodicity, based on
Shor's algorithm and a modification of a classical lower bound by Lachish and
Newman \cite{lachish&newman:periodicity}. This provides an alternative to a
recent constant-vs-polynomial speed-up due to Aaronson \cite{aaronson:bqpph}.Comment: 2nd version: updated some references, in particular to Aaronson's
Fourier checking proble
On bounding the bandwidth of graphs with symmetry
We derive a new lower bound for the bandwidth of a graph that is based on a
new lower bound for the minimum cut problem. Our new semidefinite programming
relaxation of the minimum cut problem is obtained by strengthening the known
semidefinite programming relaxation for the quadratic assignment problem (or
for the graph partition problem) by fixing two vertices in the graph; one on
each side of the cut. This fixing results in several smaller subproblems that
need to be solved to obtain the new bound. In order to efficiently solve these
subproblems we exploit symmetry in the data; that is, both symmetry in the
min-cut problem and symmetry in the graphs. To obtain upper bounds for the
bandwidth of graphs with symmetry, we develop a heuristic approach based on the
well-known reverse Cuthill-McKee algorithm, and that improves significantly its
performance on the tested graphs. Our approaches result in the best known lower
and upper bounds for the bandwidth of all graphs under consideration, i.e.,
Hamming graphs, 3-dimensional generalized Hamming graphs, Johnson graphs, and
Kneser graphs, with up to 216 vertices
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