8,315 research outputs found
Evolutionary computation of forests with Degree- and Role-Constrained Minimum Spanning Trees
Finding the degree-constrained minimum spanning tree (DCMST) of a graph is a widely studied NP-hard problem. One of its most important applications is network design. Here we deal with a new variant of the DCMST problem, which consists of finding not only the degree- but also the role-constrained minimum spanning tree (DRCMST), i.e., we add constraints to restrict the role of the nodes in the tree to root, intermediate or leaf node. Furthermore, we do not limit the number of root nodes to one, thereby, generally, building a forest of DRCMSTs. The modeling of network design problems can benefit from the possibility of generating more than one tree and determining the role of the nodes in the network. We propose a novel permutation-based representation to encode these forests. In this new representation, one permutation simultaneously encodes all the trees to be built. We simulate a wide variety of DRCMST problems which we optimize using eight different evolutionary computation algorithms encoding individuals of the population using the proposed representation. The algorithms we use are: estimation of distribution algorithm, generational genetic algorithm, steady-state genetic algorithm, covariance matrix adaptation evolution strategy, differential evolution, elitist evolution strategy, non-elitist evolution strategy and particle swarm optimization. The best results are for the estimation of distribution algorithms and both types of genetic algorithms, although the genetic algorithms are significantly faster. -------------------------------------------------------------------------------------------------- Trabajo publicado en: Antón Sánchez, Laura; Bielza Lozoya, Maria Concepcion y Larrañaga Múgica, Pedro (2017). Network Design through Forests with Degree- and Role-constrained Minimum Spanning Trees. "Journal of Heuristics ", v. 23 (n. 1); pp. 31-51. ------------------------------------------
Globally and Locally Minimal Weight Spanning Tree Networks
The competition between local and global driving forces is significant in a
wide variety of naturally occurring branched networks. We have investigated the
impact of a global minimization criterion versus a local one on the structure
of spanning trees. To do so, we consider two spanning tree structures - the
generalized minimal spanning tree (GMST) defined by Dror et al. [1] and an
analogous structure based on the invasion percolation network, which we term
the generalized invasive spanning tree or GIST. In general, these two
structures represent extremes of global and local optimality, respectively.
Structural characteristics are compared between the GMST and GIST for a fixed
lattice. In addition, we demonstrate a method for creating a series of
structures which enable one to span the range between these two extremes. Two
structural characterizations, the occupied edge density (i.e., the fraction of
edges in the graph that are included in the tree) and the tortuosity of the
arcs in the trees, are shown to correlate well with the degree to which an
intermediate structure resembles the GMST or GIST. Both characterizations are
straightforward to determine from an image and are potentially useful tools in
the analysis of the formation of network structures.Comment: 23 pages, 5 figures, 2 tables, typographical error correcte
Networks of Complements
We consider a network of sellers, each selling a single product, where the
graph structure represents pair-wise complementarities between products. We
study how the network structure affects revenue and social welfare of
equilibria of the pricing game between the sellers. We prove positive and
negative results, both of "Price of Anarchy" and of "Price of Stability" type,
for special families of graphs (paths, cycles) as well as more general ones
(trees, graphs). We describe best-reply dynamics that converge to non-trivial
equilibrium in several families of graphs, and we use these dynamics to prove
the existence of approximately-efficient equilibria.Comment: An extended abstract will appear in ICALP 201
On the performance of a cavity method based algorithm for the Prize-Collecting Steiner Tree Problem on graphs
We study the behavior of an algorithm derived from the cavity method for the
Prize-Collecting Steiner Tree (PCST) problem on graphs. The algorithm is based
on the zero temperature limit of the cavity equations and as such is formally
simple (a fixed point equation resolved by iteration) and distributed
(parallelizable). We provide a detailed comparison with state-of-the-art
algorithms on a wide range of existing benchmarks networks and random graphs.
Specifically, we consider an enhanced derivative of the Goemans-Williamson
heuristics and the DHEA solver, a Branch and Cut Linear/Integer Programming
based approach. The comparison shows that the cavity algorithm outperforms the
two algorithms in most large instances both in running time and quality of the
solution. Finally we prove a few optimality properties of the solutions
provided by our algorithm, including optimality under the two post-processing
procedures defined in the Goemans-Williamson derivative and global optimality
in some limit cases
An extensive English language bibliography on graph theory and its applications, supplement 1
Graph theory and its applications - bibliography, supplement
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Background. A large number of algorithms is being developed to reconstruct
evolutionary models of individual tumours from genome sequencing data. Most
methods can analyze multiple samples collected either through bulk multi-region
sequencing experiments or the sequencing of individual cancer cells. However,
rarely the same method can support both data types.
Results. We introduce TRaIT, a computational framework to infer mutational
graphs that model the accumulation of multiple types of somatic alterations
driving tumour evolution. Compared to other tools, TRaIT supports multi-region
and single-cell sequencing data within the same statistical framework, and
delivers expressive models that capture many complex evolutionary phenomena.
TRaIT improves accuracy, robustness to data-specific errors and computational
complexity compared to competing methods.
Conclusions. We show that the application of TRaIT to single-cell and
multi-region cancer datasets can produce accurate and reliable models of
single-tumour evolution, quantify the extent of intra-tumour heterogeneity and
generate new testable experimental hypotheses
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