27,966 research outputs found
Spectral changes in layered -electron systems induced by Kondo hole substitution in the boundary-layer
We investigate the effect of disorder on the dynamical spectrum of layered
-electron systems. With random dilution of -sites in a single Kondo
insulating layer, we explore the range and extent to which Kondo hole
incoherence can penetrate into adjacent layers. We consider three cases of
neighboring layers: band insulator, Kondo insulator and simple metal. The
disorder-induced spectral weight transfer, used here for quantification of the
proximity effect, decays algebraically with distance from the boundary layer.
Further, we show that the spectral weight transfer is highly dependent on the
frequency range considered as well as the presence of interactions in the clean
adjacent layers. The changes in the low frequency spectrum are very similar
when the adjacent layers are either metallic or Kondo insulating, and hence are
independent of interactions. In stark contrast, a distinct picture emerges for
the spectral weight transfers across large energy scales. The spectral weight
transfer over all energy scales is much higher when the adjacent layers are
non-interacting as compared to when they are strongly interacting Kondo
insulators. Thus, over all scales, interactions screen the disorder effects
significantly. We discuss the possibility of a crossover from non-Fermi liquid
to Fermi liquid behavior upon increasing the ratio of clean to disordered
layers in particle-hole asymmetric systems.Comment: 14 pages, 9 figure
Improved routing strategies for Internet traffic delivery
We analyze different strategies aimed at optimizing routing policies in the
Internet. We first show that for a simple deterministic algorithm the local
properties of the network deeply influence the time needed for packet delivery
between two arbitrarily chosen nodes. We next rely on a real Internet map at
the autonomous system level and introduce a score function that allows us to
examine different routing protocols and their efficiency in traffic handling
and packet delivery. Our results suggest that actual mechanisms are not the
most efficient and that they can be integrated in a more general, though not
too complex, scheme.Comment: Final versio
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Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running time
Constructive Heuristics for the Minimum Labelling Spanning Tree Problem: a preliminary comparison
This report studies constructive heuristics for the minimum labelling spanning tree
(MLST) problem. The purpose is to find a spanning tree that uses edges that are as similar as
possible. Given an undirected labeled connected graph (i.e., with a label or color for each edge),
the minimum labeling spanning tree problem seeks a spanning tree whose edges have the smallest
possible number of distinct labels. The model can represent many real-world problems in
telecommunication networks, electric networks, and multimodal transportation networks, among
others, and the problem has been shown to be NP-complete even for complete graphs. A primary
heuristic, named the maximum vertex covering algorithm has been proposed. Several versions of
this constructive heuristic have been proposed to improve its efficiency. Here we describe the
problem, review the literature and compare some variants of this algorithm
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Heuristics based on greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem
This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-complete. A Greedy Randomized Adaptive Search Procedure (GRASP) and different versions of Variable Neighbourhood Search (VNS) are proposed. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics
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Solving the minimum labelling spanning tree problem using hybrid local search
Given a connected, undirected graph whose edges are labelled (or coloured), the minimum
labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest
number of distinct labels (or colours). In recent work, the MLST problem has been shown
to be NP-hard and some effective heuristics (Modified Genetic Algorithm (MGA) and Pilot
Method (PILOT)) have been proposed and analyzed. A hybrid local search method, that we
call Group-Swap Variable Neighbourhood Search (GS-VNS), is proposed in this paper. It is
obtained by combining two classic metaheuristics: Variable Neighbourhood Search (VNS) and
Simulated Annealing (SA). Computational experiments show that GS-VNS outperforms MGA
and PILOT. Furthermore, a comparison with the results provided by an exact approach shows
that we may quickly obtain optimal or near-optimal solutions with the proposed heuristic
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