61,912 research outputs found
All Maximal Independent Sets and Dynamic Dominance for Sparse Graphs
We describe algorithms, based on Avis and Fukuda's reverse search paradigm,
for listing all maximal independent sets in a sparse graph in polynomial time
and delay per output. For bounded degree graphs, our algorithms take constant
time per set generated; for minor-closed graph families, the time is O(n) per
set, and for more general sparse graph families we achieve subquadratic time
per set. We also describe new data structures for maintaining a dynamic vertex
set S in a sparse or minor-closed graph family, and querying the number of
vertices not dominated by S; for minor-closed graph families the time per
update is constant, while it is sublinear for any sparse graph family. We can
also maintain a dynamic vertex set in an arbitrary m-edge graph and test the
independence of the maintained set in time O(sqrt m) per update. We use the
domination data structures as part of our enumeration algorithms.Comment: 10 page
A general method for common intervals
Given an elementary chain of vertex set V, seen as a labelling of V by the
set {1, ...,n=|V|}, and another discrete structure over , say a graph G, the
problem of common intervals is to compute the induced subgraphs G[I], such that
is an interval of [1, n] and G[I] satisfies some property Pi (as for
example Pi= "being connected"). This kind of problems comes from comparative
genomic in bioinformatics, mainly when the graph is a chain or a tree
(Heber and Stoye 2001, Heber and Savage 2005, Bergeron et al 2008).
When the family of intervals is closed under intersection, we present here
the combination of two approaches, namely the idea of potential beginning
developed in Uno, Yagiura 2000 and Bui-Xuan et al 2005 and the notion of
generator as defined in Bergeron et al 2008. This yields a very simple generic
algorithm to compute all common intervals, which gives optimal algorithms in
various applications. For example in the case where is a tree, our
framework yields the first linear time algorithms for the two properties:
"being connected" and "being a path". In the case where is a chain, the
problem is known as: common intervals of two permutations (Uno and Yagiura
2000), our algorithm provides not only the set of all common intervals but also
with some easy modifications a tree structure that represents this set
Arboricity, h-Index, and Dynamic Algorithms
In this paper we present a modification of a technique by Chiba and Nishizeki
[Chiba and Nishizeki: Arboricity and Subgraph Listing Algorithms, SIAM J.
Comput. 14(1), pp. 210--223 (1985)]. Based on it, we design a data structure
suitable for dynamic graph algorithms. We employ the data structure to
formulate new algorithms for several problems, including counting subgraphs of
four vertices, recognition of diamond-free graphs, cop-win graphs and strongly
chordal graphs, among others. We improve the time complexity for graphs with
low arboricity or h-index.Comment: 19 pages, no figure
Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost
We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial
ASMs and Operational Algorithmic Completeness of Lambda Calculus
We show that lambda calculus is a computation model which can step by step
simulate any sequential deterministic algorithm for any computable function
over integers or words or any datatype. More formally, given an algorithm above
a family of computable functions (taken as primitive tools, i.e., kind of
oracle functions for the algorithm), for every constant K big enough, each
computation step of the algorithm can be simulated by exactly K successive
reductions in a natural extension of lambda calculus with constants for
functions in the above considered family. The proof is based on a fixed point
technique in lambda calculus and on Gurevich sequential Thesis which allows to
identify sequential deterministic algorithms with Abstract State Machines. This
extends to algorithms for partial computable functions in such a way that
finite computations ending with exceptions are associated to finite reductions
leading to terms with a particular very simple feature.Comment: 37 page
Linear Time LexDFS on Cocomparability Graphs
Lexicographic depth first search (LexDFS) is a graph search protocol which
has already proved to be a powerful tool on cocomparability graphs.
Cocomparability graphs have been well studied by investigating their
complements (comparability graphs) and their corresponding posets. Recently
however LexDFS has led to a number of elegant polynomial and near linear time
algorithms on cocomparability graphs when used as a preprocessing step [2, 3,
11]. The nonlinear runtime of some of these results is a consequence of
complexity of this preprocessing step. We present the first linear time
algorithm to compute a LexDFS cocomparability ordering, therefore answering a
problem raised in [2] and helping achieve the first linear time algorithms for
the minimum path cover problem, and thus the Hamilton path problem, the maximum
independent set problem and the minimum clique cover for this graph family
Minimizing sum of completion times on a single machine with sequence-dependent family setup times
This paper presents a branch-and-bound (B&B) algorithm for minimizing the sum of completion times in a singlemachine scheduling setting with sequence-dependent family setup times. The main feature of the B&B algorithm is a new lower bounding scheme that is based on a networkformulation of the problem. With extensive computational tests, we demonstrate that the B&B algorithm can solve problems with up to 60 jobs and 12 families, where setup and processing times are uniformly distributed in various combinations of the [1,50] and [1,100] ranges
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