4,096 research outputs found
A decomposition approach for the p-median problem on disconnected graphs
The p-median problem seeks for the location of p facilities on the vertices (customers) of a graph to minimize the sum of transportation costs for satisfying the demands of the customers
from the facilities. In many real applications of the p-median problem the underlying graph is
disconnected. That is the case of p-median problem defined over split administrative regions
or regions geographically apart (e.g. archipelagos), and the case of problems coming from
industry such as the optimal diversity management problem. In such cases the problem
can be decomposed into smaller p-median problems which are solved in each component k
for different feasible values of pk, and the global solution is obtained by finding the best
combination of pk medians. This approach has the advantage that it permits to solve larger
instances since only the sizes of the connected components are important and not the size of the
whole graph. However, since the optimal number of facilities to select from each component
is not known, it is necessary to solve p-median problems for every feasible number of facilities
on each component. In this paper we give a decomposition algorithm that uses a procedure
to reduce the number of subproblems to solve. Computational tests on real instances of the
optimal diversity management problem and on simulated instances are reported showing that
the reduction of subproblems is significant, and that optimal solutions were found within
reasonable time
Computing the blocks of a quasi-median graph
Quasi-median graphs are a tool commonly used by evolutionary biologists to
visualise the evolution of molecular sequences. As with any graph, a
quasi-median graph can contain cut vertices, that is, vertices whose removal
disconnect the graph. These vertices induce a decomposition of the graph into
blocks, that is, maximal subgraphs which do not contain any cut vertices. Here
we show that the special structure of quasi-median graphs can be used to
compute their blocks without having to compute the whole graph. In particular
we present an algorithm that, for a collection of aligned sequences of
length , can compute the blocks of the associated quasi-median graph
together with the information required to correctly connect these blocks
together in run time , independent of the size of the
sequence alphabet. Our primary motivation for presenting this algorithm is the
fact that the quasi-median graph associated to a sequence alignment must
contain all most parsimonious trees for the alignment, and therefore
precomputing the blocks of the graph has the potential to help speed up any
method for computing such trees.Comment: 17 pages, 2 figure
The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search
This article presents a new search algorithm for the NP-hard problem of
optimizing functions of binary variables that decompose according to a
graphical model. It can be applied to models of any order and structure. The
main novelty is a technique to constrain the search space based on the topology
of the model. When pursued to the full search depth, the algorithm is
guaranteed to converge to a global optimum, passing through a series of
monotonously improving local optima that are guaranteed to be optimal within a
given and increasing Hamming distance. For a search depth of 1, it specializes
to Iterated Conditional Modes. Between these extremes, a useful tradeoff
between approximation quality and runtime is established. Experiments on models
derived from both illustrative and real problems show that approximations found
with limited search depth match or improve those obtained by state-of-the-art
methods based on message passing and linear programming.Comment: C++ Source Code available from
http://hci.iwr.uni-heidelberg.de/software.ph
Incremental eigenpair computation for graph Laplacian matrices: theory and applications
The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection. However, in real-life applications, the number of clusters or communities (say, K) is generally unknown a priori. Consequently, the majority of the existing methods either choose K heuristically or they repeat the clustering method with different choices of K and accept the best clustering result. The first option, more often, yields suboptimal result, while the second option is computationally expensive. In this work, we propose an incremental method for constructing the eigenspectrum of the graph Laplacian matrix. This method leverages the eigenstructure of graph Laplacian matrix to obtain the Kth smallest eigenpair of the Laplacian matrix given a collection of all previously compute
Scalable Online Betweenness Centrality in Evolving Graphs
Betweenness centrality is a classic measure that quantifies the importance of
a graph element (vertex or edge) according to the fraction of shortest paths
passing through it. This measure is notoriously expensive to compute, and the
best known algorithm runs in O(nm) time. The problems of efficiency and
scalability are exacerbated in a dynamic setting, where the input is an
evolving graph seen edge by edge, and the goal is to keep the betweenness
centrality up to date. In this paper we propose the first truly scalable
algorithm for online computation of betweenness centrality of both vertices and
edges in an evolving graph where new edges are added and existing edges are
removed. Our algorithm is carefully engineered with out-of-core techniques and
tailored for modern parallel stream processing engines that run on clusters of
shared-nothing commodity hardware. Hence, it is amenable to real-world
deployment. We experiment on graphs that are two orders of magnitude larger
than previous studies. Our method is able to keep the betweenness centrality
measures up to date online, i.e., the time to update the measures is smaller
than the inter-arrival time between two consecutive updates.Comment: 15 pages, 9 Figures, accepted for publication in IEEE Transactions on
Knowledge and Data Engineerin
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
Formulations of the Image Decomposition Problem as a Multicut Problem (MP)
w.r.t. a superpixel graph have received considerable attention. In contrast,
instances of the MP w.r.t. a pixel grid graph have received little attention,
firstly, because the MP is NP-hard and instances w.r.t. a pixel grid graph are
hard to solve in practice, and, secondly, due to the lack of long-range terms
in the objective function of the MP. We propose a generalization of the MP with
long-range terms (LMP). We design and implement two efficient algorithms
(primal feasible heuristics) for the MP and LMP which allow us to study
instances of both problems w.r.t. the pixel grid graphs of the images in the
BSDS-500 benchmark. The decompositions we obtain do not differ significantly
from the state of the art, suggesting that the LMP is a competitive formulation
of the Image Decomposition Problem. To demonstrate the generality of the LMP,
we apply it also to the Mesh Decomposition Problem posed by the Princeton
benchmark, obtaining state-of-the-art decompositions
Percolation of satisfiability in finite dimensions
The satisfiability and optimization of finite-dimensional Boolean formulas
are studied using percolation theory, rare region arguments, and boundary
effects. In contrast with mean-field results, there is no satisfiability
transition, though there is a logical connectivity transition. In part of the
disconnected phase, rare regions lead to a divergent running time for
optimization algorithms. The thermodynamic ground state for the NP-hard
two-dimensional maximum-satisfiability problem is typically unique. These
results have implications for the computational study of disordered materials.Comment: 4 pages, 4 fig
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