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This paper is motivated by operating self service transport systems
that flourish nowadays. In cities where such systems have been set
up with bikes, trucks travel to maintain a suitable number of bikes
per station.
It is natural to study a version of the C-delivery TSP defined by
Chalasani and Motwani in which, unlike their definition, C is part
of the input: each vertex v of a graph G=(V,E) has a certain
amount xv of a commodity and wishes to have an amount equal to
yv (we assume that and all
quantities...
This paper is motivated by operating self service transport systems
that flourish nowadays. In cities where such systems have been set
up with bikes, trucks travel to maintain a suitable number of bikes
per station.
It is natural to study a version of the C-delivery TSP defined by
Chalasani and Motwani in which, unlike their definition, C is part
of the input: each vertex v of a graph G=(V,E) has a certain
amount xv of a commodity and wishes to have an amount equal to
yv (we assume that and all
quantities...
Given a simple undirected weighted or unweighted graph, we try to cluster the vertex set into communities and also to quantify the robustness of these clusters. For that task, we propose a new method, called bootstrap clustering which consists in (i) defining a new clustering algorithm for graphs, (ii) building a set of graphs similar to the initial one, (iii) applying the clustering method to each of them, making a profile (set) of partitions, (iv) computing a consensus partition for this profile,...
Given a simple undirected weighted or unweighted graph, we try to cluster the vertex set into communities and also to quantify the robustness of these clusters. For that task, we propose a new method, called bootstrap clustering which consists in (i) defining a new clustering algorithm for graphs, (ii) building a set of graphs similar to the initial one, (iii) applying the clustering method to each of them, making a profile (set) of partitions, (iv) computing a consensus partition for this profile,...
We present a Branch-and-Cut algorithm where the volume algorithm is applied
instead of the traditionally used dual simplex algorithm to the linear
programming relaxations in the root node of the search tree. This means that
we use fast approximate solutions to these linear programs instead of exact
but slower solutions. We present computational results with the Steiner tree
and Max-Cut problems. We show evidence that one can solve these problems
much faster with the volume algorithm based...
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