Results 1  10
of
50
The Maximum Clique Problem
, 1999
"... Contents 1 Introduction 2 1.1 Notations and Definitions . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Problem Formulations 4 2.1 Integer Programming Formulations . . . . . . . . . . . . . . . . . . . 5 2.2 Continuous Formulations . . . . . . . . . . . . . . . . . . . . . . . . 8 3 Computation ..."
Abstract

Cited by 144 (20 self)
 Add to MetaCart
Contents 1 Introduction 2 1.1 Notations and Definitions . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Problem Formulations 4 2.1 Integer Programming Formulations . . . . . . . . . . . . . . . . . . . 5 2.2 Continuous Formulations . . . . . . . . . . . . . . . . . . . . . . . . 8 3 Computational Complexity 12 4 Bounds and Estimates 15 5 Exact Algorithms 19 5.1 Enumerative Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.2 Exact Algorithms for the Unweighted Case . . . . . . . . . . . . . . 21 5.3 Exact Algorithms for the Weighted Case . . . . . . . . . . . . . . . . 25 6 Heuristics 27 6.1 Sequential Greedy Heuristics . . . . . . . . . . . . . . . . . . . . . . 28 6.2 Local Search Heuristics . . . . . . . . . . . . . . . . . . . . . . . . . 29 6.3 Advanced Search Heuristics . . . . . . . . . . . . . . . . . . . . . . . 30 6.3.1 Simulated annealing . . . . . . . . . . . . . . . . . . . . . . . 30 6.3.2 Neural networks . . . . . . . . . . . . . . . . . . . . . . . .
The Quadratic Assignment Problem: A Survey and Recent Developments
 In Proceedings of the DIMACS Workshop on Quadratic Assignment Problems, volume 16 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1994
"... . Quadratic Assignment Problems model many applications in diverse areas such as operations research, parallel and distributed computing, and combinatorial data analysis. In this paper we survey some of the most important techniques, applications, and methods regarding the quadratic assignment probl ..."
Abstract

Cited by 91 (16 self)
 Add to MetaCart
. Quadratic Assignment Problems model many applications in diverse areas such as operations research, parallel and distributed computing, and combinatorial data analysis. In this paper we survey some of the most important techniques, applications, and methods regarding the quadratic assignment problem. We focus our attention on recent developments. 1. Introduction Given a set N = f1; 2; : : : ; ng and n \Theta n matrices F = (f ij ) and D = (d kl ), the quadratic assignment problem (QAP) can be stated as follows: min p2\Pi N n X i=1 n X j=1 f ij d p(i)p(j) + n X i=1 c ip(i) ; where \Pi N is the set of all permutations of N . One of the major applications of the QAP is in location theory where the matrix F = (f ij ) is the flow matrix, i.e. f ij is the flow of materials from facility i to facility j, and D = (d kl ) is the distance matrix, i.e. d kl represents the distance from location k to location l [62, 67, 137]. The cost of simultaneously assigning facility i to locat...
Reactive Local Search for the Maximum Clique Problem
 Algorithmica
"... A new Reactive Local Search (RLS ) algorithm is proposed for the solution of the MaximumClique problem. RLS is based on local search complemented by a feedback (historysensitive) scheme to determine the amount of diversification. The reaction acts on the single parameter that decides the temporary ..."
Abstract

Cited by 75 (14 self)
 Add to MetaCart
A new Reactive Local Search (RLS ) algorithm is proposed for the solution of the MaximumClique problem. RLS is based on local search complemented by a feedback (historysensitive) scheme to determine the amount of diversification. The reaction acts on the single parameter that decides the temporary prohibition of selected moves in the neighborhood, in a manner inspired by Tabu Search. The performance obtained in computational tests appears to be significantly better with respect to all algorithms tested at the the second DIMACS implementation challenge. The worstcase complexity per iteration of the algorithm is O(max{n, m}) where n and m are the number of nodes and edges of the graph. In practice, when a vertex is moved, the number of operations tends to be proportional to its number of missing edges and therefore the iterations are particularly fast in dense graphs.
CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions
, 2005
"... Combinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is NPcomplete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and ..."
Abstract

Cited by 49 (9 self)
 Add to MetaCart
Combinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is NPcomplete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and lower bounding (also across components), elaborate and dynamically chosen bidordering heuristics, and a host of structural observations. CABOB attempts to capture structure in any instance without making assumptions about the instance distribution. Experiments against the fastest prior algorithm, CPLEX 8.0, show that CABOB is often faster, seldom drastically slower, and in many cases drastically faster—especially in cases with structure. CABOB’s search runs in linear space and has significantly better anytime performance than CPLEX. We also uncover interesting aspects of the problem itself. First, problems with short bids, which were hard for the first generation of specialized algorithms, are easy. Second, almost all of the CATS distributions are easy, and the run time is virtually unaffected by the number of goods. Third, we test several random restart strategies, showing that they do not help on this problem—the runtime distribution does not have a heavy tail.
101 Optimal PDB Structure Alignments: a BranchandCut Algorithm for the Maximum Contact Map Overlap Problem
 Proceedings of The Fifth Annual International Conference on Computational Molecular Biology, RECOMB
, 2001
"... Structure comparison is a fundamental problem for structural genomics. A variety of structure comparison methods were proposed and several protein structure classification servers e.g., SCOP, DALI, CATH, were designed based on them, and are extensively used in practice. This area of research contin ..."
Abstract

Cited by 43 (5 self)
 Add to MetaCart
Structure comparison is a fundamental problem for structural genomics. A variety of structure comparison methods were proposed and several protein structure classification servers e.g., SCOP, DALI, CATH, were designed based on them, and are extensively used in practice. This area of research continues to be very active, being energized biannually by the CASP folding competitions, but despite the extraordinary international research effort devoted to it, progress is slow. A fundamental dimension of this bottleneck is the absence of rigorous algorithmic methods. A recent excellent survey on structure comparison by Taylor et.al. [23] records the state of the art of the area: In structure comparison, we do not even have an algorithm that guarantees an optimal answer for pairs of structures ...
ThreeDimensional Orthogonal Graph Drawing
, 2000
"... vi Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Tables . . . . . . . . . . . . ..."
Abstract

Cited by 27 (10 self)
 Add to MetaCart
vi Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii List of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv I Orthogonal Graph Drawing 1 1
An Algorithm for Finding a Maximum Clique in a Graph
, 1997
"... This paper introduces a branchandbound algorithm for the maximum clique problem which applies existing clique finding and vertex coloring heuristics to determine lower and upper bounds for the size of a maximum clique. Computational results on a variety of graphs indicate the proposed procedure in ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
This paper introduces a branchandbound algorithm for the maximum clique problem which applies existing clique finding and vertex coloring heuristics to determine lower and upper bounds for the size of a maximum clique. Computational results on a variety of graphs indicate the proposed procedure in most instances outperforms leading algorithms.
Approximating Maximum Clique with a Hopfield Network
 IEEE Trans. Neural Networks
, 1995
"... In a graph, a clique is a set of vertices such that every pair is connected by an edge. MAXCLIQUE is the optimization problem of finding the largest clique in a given graph, and is NPhard, even to approximate well. Several realworld and theory problems can be modeled as MAXCLIQUE. In this paper, ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
In a graph, a clique is a set of vertices such that every pair is connected by an edge. MAXCLIQUE is the optimization problem of finding the largest clique in a given graph, and is NPhard, even to approximate well. Several realworld and theory problems can be modeled as MAXCLIQUE. In this paper, we efficiently approximate MAXCLIQUE in a special case of the Hopfield Network whose stable states are maximal cliques. We present several energydescent optimizing dynamics; both discrete (deterministic and stochastic) and continuous. One of these emulates, as special cases, two well known greedy algorithms for approximating MAXCLIQUE. We report on detailed empirical comparisons on random graphs. MeanField Annealingan efficient approximation to Simulated Annealingand a stochastic dynamics are the narrow but clear winners. All dynamics approximate much better than one which emulates a "naive" greedy heuristic. 1 Cliques and Maximum Clique In a graph with undirected edges, a cliq...
Optimized Crossover for the Independent Set Problem
, 1995
"... We propose a knowledgebased crossover mechanism for genetic algorithms that exploits the structure of the solution rather than its coding. More generally, we suggest broad guidelines for constructing the knowledgebased crossover mechanisms. This technique uses an optimized crossover mechanism, in ..."
Abstract

Cited by 20 (3 self)
 Add to MetaCart
We propose a knowledgebased crossover mechanism for genetic algorithms that exploits the structure of the solution rather than its coding. More generally, we suggest broad guidelines for constructing the knowledgebased crossover mechanisms. This technique uses an optimized crossover mechanism, in which the one of the two children is constructed in such a way so as to have the best objective function value from the feasible set of children, while the other is constructed so as to maintain the diversity of the search space. We implement our approach on a classical combinatorial problem, called the independent set problem. The resulting genetic algorithm dominates all other genetic algorithms for the problem, and yields one of the best heuristics for the independent set problem in terms of robustness and time performance.
Simple and Fast: Improving a BranchandBound Algorithm for Maximum Clique
 In 10th Annual European Symposium on Algorithms (ESA 2002), volume 2461 of LNCS
, 2002
"... We consider a branchandbound algorithm for maximum clique problems. ..."
Abstract

Cited by 18 (0 self)
 Add to MetaCart
We consider a branchandbound algorithm for maximum clique problems.