12,841 research outputs found
Finding edge-disjoint paths in networks by means of artificial ant colonies
One of the basic operations in communication networks consists in establishing routes for connection requests between physically separated network nodes. In many situations, either due to technical constraints or to quality-of-service and survivability requirements, it is required that no two routes interfere with each other. These requirements apply in particular to routing and admission control in large-scale, high-speed and optical networks. The same requirements also arise in a multitude of other applications such as real-time communications, VLSI design, scheduling, bin packing, and load balancing. This problem can be modeled as a combinatorial optimization problem as follows. Given a graph G representing a network topology, and a collection T={(s_1,t_1)...(s_k,t_k)} of pairs of vertices in G representing connection request, the maximum edge-disjoint paths problem is an NP-hard problem that consists in determining the maximum number of pairs in T that can be routed in G by mutually edge-disjoint s_i-t_i paths. We propose an ant colony optimization (ACO) algorithm to solve this problem. ACO algorithms are approximate algorithms that are inspired by the foraging behavior of real ants. The decentralized nature of these algorithms makes them suitable for the application to problems arising in large-scale environments. First, we propose a basic version of our algorithm in order to outline its main features. In a subsequent step we propose several extensions of the basic algorithm and we conduct an extensive parameter tuning in order to show the usefulness of those extensions. In comparison to a multi-start greedy approach, our algorithm generates in general solutions of higher quality in a shorter amount of time. In particular the run-time behaviour of our algorithm is one of its important advantages.Postprint (published version
Finding edge-disjoint paths with artificial ant colonies
One of the basic operations in communication networks consists in establishing routes
for connection requests between physically separated network nodes. In many situations,
either due to technical constraints or to quality-of-service and survivability requirements, it is
required that no two routes interfere with each other. These requirements apply in particular
to routing and admission control in large-scale, high-speed and optical networks. The same
requirements also arise in a multitude of other applications such as real-time communications,
vlsi design, scheduling, bin packing, and load balancing. This problem can be modeled as
a combinatorial optimization problem as follows. Given a graph G representing a network
topology, and a collection T = f(s1; t1) : : : (sk; tk)g of pairs of vertices in G representing
connection request, the maximum edge-disjoint paths problem is an NP-hard problem that
consists in determining the maximum number of pairs in T that can be routed in G by
mutually edge-disjoint si - ti paths.
We propose an ant colony optimization (aco) algorithm to solve this problem. aco algo-
rithms are approximate algorithms that are inspired by the foraging behavior of real ants. The
decentralized nature of these algorithms makes them suitable for the application to problems
arising in large-scale environments. First, we propose a basic version of our algorithm in order
to outline its main features. In a subsequent step we propose several extensions of the basic
algorithm and we conduct an extensive parameter tuning in order to show the usefulness of
those extensions. In comparison to a multi-start greedy approach, our algorithm generates
in general solutions of higher quality in a shorter amount of time. In particular the run-time
behaviour of our algorithm is one of its important advantages.Postprint (published version
Counting Shortest Two Disjoint Paths in Cubic Planar Graphs with an NC Algorithm
Given an undirected graph and two disjoint vertex pairs and
, the Shortest two disjoint paths problem (S2DP) asks for the minimum
total length of two vertex disjoint paths connecting with , and
with , respectively.
We show that for cubic planar graphs there are NC algorithms, uniform
circuits of polynomial size and polylogarithmic depth, that compute the S2DP
and moreover also output the number of such minimum length path pairs.
Previously, to the best of our knowledge, no deterministic polynomial time
algorithm was known for S2DP in cubic planar graphs with arbitrary placement of
the terminals. In contrast, the randomized polynomial time algorithm by
Bj\"orklund and Husfeldt, ICALP 2014, for general graphs is much slower, is
serial in nature, and cannot count the solutions.
Our results are built on an approach by Hirai and Namba, Algorithmica 2017,
for a generalisation of S2DP, and fast algorithms for counting perfect
matchings in planar graphs
Edge- and Node-Disjoint Paths in P Systems
In this paper, we continue our development of algorithms used for topological
network discovery. We present native P system versions of two fundamental
problems in graph theory: finding the maximum number of edge- and node-disjoint
paths between a source node and target node. We start from the standard
depth-first-search maximum flow algorithms, but our approach is totally
distributed, when initially no structural information is available and each P
system cell has to even learn its immediate neighbors. For the node-disjoint
version, our P system rules are designed to enforce node weight capacities (of
one), in addition to edge capacities (of one), which are not readily available
in the standard network flow algorithms.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
Scheduling MapReduce Jobs under Multi-Round Precedences
We consider non-preemptive scheduling of MapReduce jobs with multiple tasks
in the practical scenario where each job requires several map-reduce rounds. We
seek to minimize the average weighted completion time and consider scheduling
on identical and unrelated parallel processors. For identical processors, we
present LP-based O(1)-approximation algorithms. For unrelated processors, the
approximation ratio naturally depends on the maximum number of rounds of any
job. Since the number of rounds per job in typical MapReduce algorithms is a
small constant, our scheduling algorithms achieve a small approximation ratio
in practice. For the single-round case, we substantially improve on previously
best known approximation guarantees for both identical and unrelated
processors. Moreover, we conduct an experimental analysis and compare the
performance of our algorithms against a fast heuristic and a lower bound on the
optimal solution, thus demonstrating their promising practical performance
A polynomial delay algorithm for the enumeration of bubbles with length constraints in directed graphs and its application to the detection of alternative splicing in RNA-seq data
We present a new algorithm for enumerating bubbles with length constraints in
directed graphs. This problem arises in transcriptomics, where the question is
to identify all alternative splicing events present in a sample of mRNAs
sequenced by RNA-seq. This is the first polynomial-delay algorithm for this
problem and we show that in practice, it is faster than previous approaches.
This enables us to deal with larger instances and therefore to discover novel
alternative splicing events, especially long ones, that were previously
overseen using existing methods.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
Socially Constrained Structural Learning for Groups Detection in Crowd
Modern crowd theories agree that collective behavior is the result of the
underlying interactions among small groups of individuals. In this work, we
propose a novel algorithm for detecting social groups in crowds by means of a
Correlation Clustering procedure on people trajectories. The affinity between
crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their
physical and social identity, inspired by Proxemic theory, Granger causality,
DTW and Heat-maps. To adhere to sociological observations, we introduce a loss
function (G-MITRE) able to deal with the complexity of evaluating group
detection performances. We show our algorithm achieves state-of-the-art results
when relying on both ground truth trajectories and tracklets previously
extracted by available detector/tracker systems
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