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18
Graph Minimum Linear Arrangement by Multilevel Weighted Edge Contractions
, 2006
"... The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. In this paper we present a lineartime algorithm for the problem inspired by the algebraic multigrid approach which is based on weighted edge contraction rather than simple contraction. ..."
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Cited by 20 (7 self)
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The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. In this paper we present a lineartime algorithm for the problem inspired by the algebraic multigrid approach which is based on weighted edge contraction rather than simple contraction. Our results turned out to be better than every known result in almost all cases, while the short running time of the algorithm enabled experiments with very large graphs.
Heuristics and Experimental Design for Bigraph Crossing Number Minimization
 IN ALGORITHM ENGINEERING AND EXPERIMENTATION (ALENEX’99), NUMBER 1619 IN LECTURE NOTES IN COMPUTER SCIENCE
, 1999
"... The bigraph crossing problem, embedding the two vertex sets of a bipartite graph G = (V0;V1;E) along two parallel lines so that edge crossings are minimized, has application to circuit layout and graph drawing. We consider the case where both V0 and V1 can be permuted arbitrarily  both this and ..."
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Cited by 14 (9 self)
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The bigraph crossing problem, embedding the two vertex sets of a bipartite graph G = (V0;V1;E) along two parallel lines so that edge crossings are minimized, has application to circuit layout and graph drawing. We consider the case where both V0 and V1 can be permuted arbitrarily  both this and the case where the order of one vertex set is fixed are NPhard. Two new heuristics that perform well on sparse graphs such as occur in circuit layout problems are presented. The new heuristics outperform existing heuristics on graph classes that range from applicationspecific to random. Our experimental design methodology ensures that differences in performance are statistically significant and not the result of minor variations in graph structure or input order.
Multilevel algorithms for linear ordering problems
, 2007
"... Linear ordering problems are combinatorial optimization problems which deal with the minimization of different functionals in which the graph vertices are mapped onto (1, 2,..., n). These problems are widely used and studied in many practical and theoretical applications. In this paper we present a ..."
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Cited by 12 (7 self)
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Linear ordering problems are combinatorial optimization problems which deal with the minimization of different functionals in which the graph vertices are mapped onto (1, 2,..., n). These problems are widely used and studied in many practical and theoretical applications. In this paper we present a variety of lineartime algorithms for these problems inspired by the Algebraic Multigrid approach which is based on weighted edge contraction. The experimental result for four such problems turned out to be better than every known result in almost all cases, while the short running time of the algorithms enables testing very large graphs.
A FixedParameter Approach to TwoLayer Planarization
, 2002
"... A bipartite graph is biplanar if the vertices can be placed on two parallel lines (layers) in the plane such that there are no edge crossings when edges are drawn straight. The 2Layer Planarization problem asks if k edges can be deleted from a given graph G so that the remaining graph is biplanar ..."
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Cited by 11 (2 self)
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A bipartite graph is biplanar if the vertices can be placed on two parallel lines (layers) in the plane such that there are no edge crossings when edges are drawn straight. The 2Layer Planarization problem asks if k edges can be deleted from a given graph G so that the remaining graph is biplanar. This problem is NPcomplete, as is the 1Layer Planarization problem in which the permutation of the vertices in one layer is fixed. We give the following fixed parameter tractability results: an O(k ·6 k +G) algorithm for 2Layer Planarization and an O(3 k ·G) algorithm for 1Layer Planarization, thus achieving linear time for fixed k.
Heuristics, Experimental Subjects, and Treatment Evaluation in Bigraph Crossing Minimization
"... ..."
Evaluating Iterative Improvement Heuristics for Bigraph Crossing Minimization
 In IEEE 1999 International Symposium on Circuits and Systems  ISCAS'99
, 1999
"... Abstract { The bigraph crossing problem, embedding the two node sets of a bipartite graph G =(V0;V1;E) along two parallel lines so that edge crossings are minimized, has application to placement optimization for standard cells and other technologies. Iterative improvement heuristics involve repeated ..."
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Cited by 4 (3 self)
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Abstract { The bigraph crossing problem, embedding the two node sets of a bipartite graph G =(V0;V1;E) along two parallel lines so that edge crossings are minimized, has application to placement optimization for standard cells and other technologies. Iterative improvement heuristics involve repeated application of some transformation on an existing feasible solution to obtain better feasible solutions. Typically an increase in the number of iterations, and therefore execution time, implies an improvement in solution quality. We investigate tradeo s between execution time and solution quality in order to establish the best heuristic for any given time budget. Our experiments show some clear trends for a scalable class of graphs based on actual circuits. These trends, based on statistically signi cant samples of each of several graph graph sizes, suggest promising directions for development of better heuristics.
TwoLayer Planarization Parameterized by Feedback Edge Set
"... Given an undirected graph G and an integer k ≥ 0, the NPhard 2Layer Planarization problem asks whether G can be transformed into a forest of caterpillar trees by removing at most k edges. Since transforming G into a forest of caterpillar trees requires breaking every cycle, the size f of a minimum ..."
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Cited by 3 (0 self)
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Given an undirected graph G and an integer k ≥ 0, the NPhard 2Layer Planarization problem asks whether G can be transformed into a forest of caterpillar trees by removing at most k edges. Since transforming G into a forest of caterpillar trees requires breaking every cycle, the size f of a minimum feedback edge set is a natural parameter with f ≤ k. We improve on previous fixedparameter tractability results with respect to k by presenting a problem kernel with O(f) vertices and edges and a new searchtree based algorithm, both with about the same worstcase bounds for f as the previous results for k, although we expect f to be smaller than k for a wide range of input instances.
Generation of Tightly Controlled Circuit Classes for Problems in Physical Design
 CBL, CS Dept., NCSU, Box 8206
, 2000
"... This paper is motivated by the need to generate classes of circuits that are closely related. Ideally, such circuits not only have identical size and distributions of cells and nets but also resemble each other closely in terms of cell clusters and interconnect patterns. Such classes are essential f ..."
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Cited by 2 (1 self)
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This paper is motivated by the need to generate classes of circuits that are closely related. Ideally, such circuits not only have identical size and distributions of cells and nets but also resemble each other closely in terms of cell clusters and interconnect patterns. Such classes are essential for welldefined performance evaluation of layout algorithms. For example, if the heuristic in the algorithm is being optimized for multiplier structures, then all circuits in the class should have a multiplierlike structure. Such tight control of circuit class generation has not been demonstrated with recent techniques, including the generation of clone and mutant classes. We introduce an approach to generating circuit classes called siblings. Siblings retain all significant characteristics of their parent circuit. We demonstrate that siblings inherit the essential characteristics of the parent only if we control, for each connected component, the diameter of the clusters and the arrangement ...