63,009 research outputs found
Comparing and simplifying distinct-cluster phylogenetic networks
Phylogenetic networks are rooted acyclic directed graphs in which the leaves
are identified with members of a set X of species. The cluster of a vertex is
the set of leaves that are descendants of the vertex. A network is
"distinct-cluster" if distinct vertices have distinct clusters. This paper
focuses on the set DC(X) of distinct-cluster networks whose leaves are
identified with the members of X. For a fixed X, a metric on DC(X) is defined.
There is a "cluster-preserving" simplification process by which vertices or
certain arcs may be removed without changing the clusters of any remaining
vertices. Many of the resulting networks may be uniquely determined without
regard to the order of the simplifying operations.Comment: This is version 2. A previous version is already on ArXi
Compact Oblivious Routing
Oblivious routing is an attractive paradigm for large distributed systems in which centralized control and frequent reconfigurations are infeasible or undesired (e.g., costly). Over the last almost 20 years, much progress has been made in devising oblivious routing schemes that guarantee close to optimal load and also algorithms for constructing such schemes efficiently have been designed. However, a common drawback of existing oblivious routing schemes is that they are not compact: they require large routing tables (of polynomial size), which does not scale.
This paper presents the first oblivious routing scheme which guarantees close to optimal load and is compact at the same time - requiring routing tables of polylogarithmic size. Our algorithm maintains the polylogarithmic competitive ratio of existing algorithms, and is hence particularly well-suited for emerging large-scale networks
On Computing the Maximum Parsimony Score of a Phylogenetic Network
Phylogenetic networks are used to display the relationship of different
species whose evolution is not treelike, which is the case, for instance, in
the presence of hybridization events or horizontal gene transfers. Tree
inference methods such as Maximum Parsimony need to be modified in order to be
applicable to networks. In this paper, we discuss two different definitions of
Maximum Parsimony on networks, "hardwired" and "softwired", and examine the
complexity of computing them given a network topology and a character. By
exploiting a link with the problem Multicut, we show that computing the
hardwired parsimony score for 2-state characters is polynomial-time solvable,
while for characters with more states this problem becomes NP-hard but is still
approximable and fixed parameter tractable in the parsimony score. On the other
hand we show that, for the softwired definition, obtaining even weak
approximation guarantees is already difficult for binary characters and
restricted network topologies, and fixed-parameter tractable algorithms in the
parsimony score are unlikely. On the positive side we show that computing the
softwired parsimony score is fixed-parameter tractable in the level of the
network, a natural parameter describing how tangled reticulate activity is in
the network. Finally, we show that both the hardwired and softwired parsimony
score can be computed efficiently using Integer Linear Programming. The
software has been made freely available
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