92,828 research outputs found
Optimal Embedding of Functions for In-Network Computation: Complexity Analysis and Algorithms
We consider optimal distributed computation of a given function of
distributed data. The input (data) nodes and the sink node that receives the
function form a connected network that is described by an undirected weighted
network graph. The algorithm to compute the given function is described by a
weighted directed acyclic graph and is called the computation graph. An
embedding defines the computation communication sequence that obtains the
function at the sink. Two kinds of optimal embeddings are sought, the embedding
that---(1)~minimizes delay in obtaining function at sink, and (2)~minimizes
cost of one instance of computation of function. This abstraction is motivated
by three applications---in-network computation over sensor networks, operator
placement in distributed databases, and module placement in distributed
computing.
We first show that obtaining minimum-delay and minimum-cost embeddings are
both NP-complete problems and that cost minimization is actually MAX SNP-hard.
Next, we consider specific forms of the computation graph for which polynomial
time solutions are possible. When the computation graph is a tree, a polynomial
time algorithm to obtain the minimum delay embedding is described. Next, for
the case when the function is described by a layered graph we describe an
algorithm that obtains the minimum cost embedding in polynomial time. This
algorithm can also be used to obtain an approximation for delay minimization.
We then consider bounded treewidth computation graphs and give an algorithm to
obtain the minimum cost embedding in polynomial time
On the phase transitions of graph coloring and independent sets
We study combinatorial indicators related to the characteristic phase
transitions associated with coloring a graph optimally and finding a maximum
independent set. In particular, we investigate the role of the acyclic
orientations of the graph in the hardness of finding the graph's chromatic
number and independence number. We provide empirical evidence that, along a
sequence of increasingly denser random graphs, the fraction of acyclic
orientations that are `shortest' peaks when the chromatic number increases, and
that such maxima tend to coincide with locally easiest instances of the
problem. Similar evidence is provided concerning the `widest' acyclic
orientations and the independence number
Improved Optimal and Approximate Power Graph Compression for Clearer Visualisation of Dense Graphs
Drawings of highly connected (dense) graphs can be very difficult to read.
Power Graph Analysis offers an alternate way to draw a graph in which sets of
nodes with common neighbours are shown grouped into modules. An edge connected
to the module then implies a connection to each member of the module. Thus, the
entire graph may be represented with much less clutter and without loss of
detail. A recent experimental study has shown that such lossless compression of
dense graphs makes it easier to follow paths. However, computing optimal power
graphs is difficult. In this paper, we show that computing the optimal
power-graph with only one module is NP-hard and therefore likely NP-hard in the
general case. We give an ILP model for power graph computation and discuss why
ILP and CP techniques are poorly suited to the problem. Instead, we are able to
find optimal solutions much more quickly using a custom search method. We also
show how to restrict this type of search to allow only limited back-tracking to
provide a heuristic that has better speed and better results than previously
known heuristics.Comment: Extended technical report accompanying the PacificVis 2013 paper of
the same nam
Quantifying the Extent of Lateral Gene Transfer Required to Avert a `Genome of Eden'
The complex pattern of presence and absence of many genes across different
species provides tantalising clues as to how genes evolved through the
processes of gene genesis, gene loss and lateral gene transfer (LGT). The
extent of LGT, particularly in prokaryotes, and its implications for creating a
`network of life' rather than a `tree of life' is controversial. In this paper,
we formally model the problem of quantifying LGT, and provide exact
mathematical bounds, and new computational results. In particular, we
investigate the computational complexity of quantifying the extent of LGT under
the simple models of gene genesis, loss and transfer on which a recent
heuristic analysis of biological data relied. Our approach takes advantage of a
relationship between LGT optimization and graph-theoretical concepts such as
tree width and network flow
Static Output Feedback: On Essential Feasible Information Patterns
In this paper, for linear time-invariant plants, where a collection of
possible inputs and outputs are known a priori, we address the problem of
determining the communication between outputs and inputs, i.e., information
patterns, such that desired control objectives of the closed-loop system (for
instance, stabilizability) through static output feedback may be ensured.
We address this problem in the structural system theoretic context. To this
end, given a specified structural pattern (locations of zeros/non-zeros) of the
plant matrices, we introduce the concept of essential information patterns,
i.e., communication patterns between outputs and inputs that satisfy the
following conditions: (i) ensure arbitrary spectrum assignment of the
closed-loop system, using static output feedback constrained to the information
pattern, for almost all possible plant instances with the specified structural
pattern; and (ii) any communication failure precludes the resulting information
pattern from attaining the pole placement objective in (i).
Subsequently, we study the problem of determining essential information
patterns. First, we provide several necessary and sufficient conditions to
verify whether a specified information pattern is essential or not. Further, we
show that such conditions can be verified by resorting to algorithms with
polynomial complexity (in the dimensions of the state, input and output).
Although such verification can be performed efficiently, it is shown that the
problem of determining essential information patterns is in general NP-hard.
The main results of the paper are illustrated through examples
Fast Distributed Approximation for Max-Cut
Finding a maximum cut is a fundamental task in many computational settings.
Surprisingly, it has been insufficiently studied in the classic distributed
settings, where vertices communicate by synchronously sending messages to their
neighbors according to the underlying graph, known as the or
models. We amend this by obtaining almost optimal
algorithms for Max-Cut on a wide class of graphs in these models. In
particular, for any , we develop randomized approximation
algorithms achieving a ratio of to the optimum for Max-Cut on
bipartite graphs in the model, and on general graphs in the
model.
We further present efficient deterministic algorithms, including a
-approximation for Max-Dicut in our models, thus improving the best known
(randomized) ratio of . Our algorithms make non-trivial use of the greedy
approach of Buchbinder et al. (SIAM Journal on Computing, 2015) for maximizing
an unconstrained (non-monotone) submodular function, which may be of
independent interest
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