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114
Evolutionary Algorithms and the Maximum Matching Problem
, 2002
"... Randomized search heuristics like evolutionary algorithms are mostly applied to problems whose structure is not completely known but also to combinatorial optimization problems. Practitioners report surprising successes but almost no results with theoretically wellfounded analyses exist. Such a ..."
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Cited by 65 (10 self)
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Randomized search heuristics like evolutionary algorithms are mostly applied to problems whose structure is not completely known but also to combinatorial optimization problems. Practitioners report surprising successes but almost no results with theoretically wellfounded analyses exist. Such an analysis is started in this paper for a fundamental evolutionary algorithm and the wellknown maximum matching problem. It is
Instruction Generation for Hybrid Reconfigurable Systems
 ACM Transactions on Design Automation of Electronic Systems
, 2001
"... Building Blocks (ABBs), or instructions available from a given hardware library. The customized data path generated from many ABBs was referred to as an application specific unit (ASU). Cathedral's synthesis targeted ASUs, which could be executed in very few clock cycles. This goal was achieved via ..."
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Cited by 63 (6 self)
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Building Blocks (ABBs), or instructions available from a given hardware library. The customized data path generated from many ABBs was referred to as an application specific unit (ASU). Cathedral's synthesis targeted ASUs, which could be executed in very few clock cycles. This goal was achieved via manual clustering of necessary operations into more compact operations, essentially a form of template construction. Whereas our template generation and matching algorithms are automated, the definition of clusters in Cathedral was a manual operation, mainly clustering loop and function bodies. Their results demonstrated an expected reduction of critical path length as well as interconnect as a result of clustering.
An experimental study of data migration algorithms. Algorithm Engineering
 the Proceedings of WAE 2001: 5th Workshop on Algorithm Engineering (BRICS, University of Aarhus
, 2001
"... Abstract. The data migration problem is the problem ofcomputing a plan for moving data objects stored on devices in a network from one configuration to another. Load balancing or changing usage patterns might necessitate such a rearrangement ofdata. In this paper, we consider the case where the obje ..."
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Cited by 48 (5 self)
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Abstract. The data migration problem is the problem ofcomputing a plan for moving data objects stored on devices in a network from one configuration to another. Load balancing or changing usage patterns might necessitate such a rearrangement ofdata. In this paper, we consider the case where the objects are fixedsize and the network is complete. We introduce two new data migration algorithms, one ofwhich has provably good bounds. We empirically compare the performance of these new algorithms against similar algorithms from Hall et al. [7] which have better theoretical guarantees and find that in almost all cases, the new algorithms perform better. We also find that both the new algorithms and the ones from Hall et al. perform much better in practice than the theoretical bounds suggest. 1
The alldifferent Constraint: A Survey
, 2001
"... The constraint of difference is known to the constraint programming community since Lauriere introduced Alice in 1978. Since then, several strategies have been designed to solve the alldifferent constraint. This paper surveys the most important developments over the years regarding the alldifferent ..."
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Cited by 42 (1 self)
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The constraint of difference is known to the constraint programming community since Lauriere introduced Alice in 1978. Since then, several strategies have been designed to solve the alldifferent constraint. This paper surveys the most important developments over the years regarding the alldifferent constraint. First we summarize the underlying concepts and results from graph theory and integer programming. Then we give an overview and an abstract comparison of different solution strategies. In addition, the symmetric alldifferent constraint is treated. Finally, we show how to apply costbased filtering to the alldifferent constraint.
Finding graph matchings in data streams
 APPROXRANDOM
, 2005
"... Abstract. We present algorithms for finding large graph matchings in the streaming model. In this model, applicable when dealing with massive graphs, edges are streamedin in some arbitrary order rather than 1 ..."
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Cited by 31 (8 self)
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Abstract. We present algorithms for finding large graph matchings in the streaming model. In this model, applicable when dealing with massive graphs, edges are streamedin in some arbitrary order rather than 1
Maximum matchings in planar graphs via Gaussian elimination
 ALGORITHMICA
, 2004
"... We present a randomized algorithm for finding maximum matchings in planar graphs in time O(n ω/2), where ω is the exponent of the best known matrix multiplication algorithm. Since ω < 2.38, this algorithm breaks through the O(n 1.5) barrier for the matching problem. This is the first result of this ..."
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Cited by 16 (2 self)
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We present a randomized algorithm for finding maximum matchings in planar graphs in time O(n ω/2), where ω is the exponent of the best known matrix multiplication algorithm. Since ω < 2.38, this algorithm breaks through the O(n 1.5) barrier for the matching problem. This is the first result of this kind for general planar graphs. We also present an algorithm for generating perfect matchings in planar graphs uniformly at random using O(n ω/2) arithmetic operations. Our algorithms are based on the Gaussian elimination approach to maximum matchings introduced in [1].
Parameterized complexity of cardinality constrained optimization problems
, 2006
"... We study the parameterized complexity of cardinality constrained optimization problems, i.e. optimization problems that require their solutions to contain specified numbers of elements to optimize solution values. For this purpose, we consider around 20 such optimization problems, as well as their p ..."
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Cited by 16 (2 self)
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We study the parameterized complexity of cardinality constrained optimization problems, i.e. optimization problems that require their solutions to contain specified numbers of elements to optimize solution values. For this purpose, we consider around 20 such optimization problems, as well as their parametric duals, that deal with various fundamental relations among vertices and edges in graphs. We have almost completely settled their parameterized complexity by giving either FPT algorithms or W[1]hardness proofs. Furthermore, we obtain faster exact algorithms for several cardinality constrained optimization problems by transforming them into problems of finding maximum (minimum) weight triangles in weighted graphs.
A Framework for Identifying Compromised Nodes in Wireless Sensor Networks
"... Sensor networks are often subject to physical attacks. Once a node’s cryptographic key is compromised, an attacker may completely impersonate it and introduce arbitrary false information into the network. Basic cryptographic mechanisms are often not effective in this situation. Most techniques to ad ..."
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Cited by 15 (3 self)
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Sensor networks are often subject to physical attacks. Once a node’s cryptographic key is compromised, an attacker may completely impersonate it and introduce arbitrary false information into the network. Basic cryptographic mechanisms are often not effective in this situation. Most techniques to address this problem focus on detecting and tolerating false information introduced by compromised nodes. They cannot pinpoint exactly where the false information is introduced and who is responsible for it. In this article, we propose an applicationindependent framework for accurately identifying compromised sensor nodes. The framework provides an appropriate abstraction of applicationspecific detection mechanisms and models the unique properties of sensor networks. Based on the framework, we develop alert reasoning algorithms to identify compromised nodes. The algorithm assumes that compromised nodes may collude at will. We show that our algorithm is optimal in the sense that it identifies the largest number of compromised nodes without introducing false