Michael Jünger

Graph Drawing Software

References.- Technical Foundations.- 1 Introduction.- 2 Graphs and Their Representation.- 3 Graph Planarity and Embeddings.- 4 Graph Drawing Methods.- References.- WilmaScope - A 3D Graph Visualization System.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Pajek - Analysis and Visualization of Large Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Tulip - A Huge Graph Visualization Framework.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Graphviz and Dynagraph - Static and Dynamic Graph Drawing Tools.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- AGD - A Library of Algorithms for Graph Drawing.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- yFiles - Visualization and Automatic Layout of Graphs.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- GDS - A Graph Drawing Server on the Internet.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- BioPath - Exploration and Visualization of Biochemical Pathways.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- DBdraw - Automatic Layout of Relational Database Schemas.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- GoVisual - A Diagramming Software for UML Class Diagrams.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- CrocoCosmos - 3D Visualization of Large Object-oriented Programs.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- ViSta - Visualizing Statecharts.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- visone - Analysis and Visualization of Social Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.- Polyphemus and Hermes - Exploration and Visualization of Computer Networks.- 1 Introduction.- 2 Applications.- 3 Algorithms.- 4 Implementation.- 5 Examples.- 6 Software.- References.

A Cutting Plane Algorithm for the Linear Ordering Problem

The linear ordering problem is an NP-hard combinatorial optimization problem with a large number of applications including triangulation of input-output matrices, archaeological senation, minimizing total weighted completion time in one-machine scheduling, and aggregation of individual preferences. In a former paper, we have investigated the facet structure of the 0/1-polytope associated with the linear ordering problem. Here we report on a new algorithm that is based on these theoretical results. The main part of the algorithm is a cutting plane procedure using facet defining inequalities. This procedure is combined with various heuristics and branch and bound techniques. Our computational results compare favorably with the results of existing codes. In particular, we could triangulate all input-output matrices, of size up to 60 × 60, available to us within acceptable time bounds.

An application of combinatorial optimization to statistical physics and circuit layout design

We study the problem of finding ground states of spin glasses with exterior magnetic field, and the problem of minimizing the number of vias holes on a printed circuit board, or contacts on a chip subject to pin preassignments and layer preferences. The former problem comes up in solid-state physics, and the latter in very-large-scale-integrated VLSI circuit design and in printed circuit board design. Both problems can be reduced to the max-cut problem in graphs. Based on a partial characterization of the cut polytope, we design a cutting plane algorithm and report on computational experience with it. Our method has been used to solve max-cut problems on graphs with up to 1,600 nodes.

2-layer straightline crossing minimization: Performance of exact and heuristic algorithms.

We present algorithms for the two layer straightline crossing minimization problem that are able to compute exact optima. Our computational results lead us to the conclusion that there is no need for heuristics if one layer is xed, even though the problem is NP-hard, and that for the general problem with two variable layers, true optima can be computed for sparse instances in which the smaller layer contains up to 15 nodes. For bigger instances, the iterated barycenter method turns out to be the method of choice among several popular heuristics whose performance we could assess by comparing their results to optimum solutions.

Experiments in quadratic 0-1 programming

We present computational experience with a cutting plane algorithm for 0–1 quadratic programming without constraints. Our approach is based on a reduction of this problem to a max-cut problem in a graph and on a partial linear description of the cut polytope.

Drawing large graphs with a potential-field-based multilevel algorithm

Force-directed graph drawing algorithms are widely used for drawing general graphs. However, these methods do not guarantee a sub-quadratic running time in general. We present a new force-directed method that is based on a combination of an efficient multilevel scheme and a strategy for approximating the repulsive forces in the system by rapidly evaluating potential fields. Given a graph G =( V,E), the asymp- totic worst case running time of this method is O(|V | log |V | +|E| )w ith linear memory requirements. In practice, the algorithm generates nice drawings of graphs containing 100000 nodes in less than 5 minutes. Fur- thermore, it clearly visualizes even the structures of those graphs that turned out to be challenging for some other methods.

Facets of the linear ordering polytope

LetDn be the complete digraph onn nodes, and letPLOn denote the convex hull of all incidence vectors of arc sets of linear orderings of the nodes ofDn (i.e. these are exactly the acyclic tournaments ofDn). We show that various classes of inequalities define facets ofPLOn, e.g. the 3-dicycle inequalities, the simplek-fence inequalities and various Möbius ladder inequalities, and we discuss the use of these inequalities in cutting plane approaches to the triangulation problem of input-output matrices, i.e. the solution of permutation resp. linear ordering problems.

50 Years of Integer Programming 1958-2008

I The Early Years.- Solution of a Large-Scale Traveling-Salesman Problem.- The Hungarian Method for the Assignment Problem.- Integral Boundary Points of Convex Polyhedra.- Outline of an Algorithm for Integer Solutions to Linear Programs An Algorithm for the Mixed Integer Problem.- An Automatic Method for Solving Discrete Programming Problems.- Integer Programming: Methods, Uses, Computation.- Matroid Partition.- Reducibility Among Combinatorial Problems.- Lagrangian Relaxation for Integer Programming.- Disjunctive Programming.- II From the Beginnings to the State-of-the-Art.- Polyhedral Approaches to Mixed Integer Linear Programming.- Fifty-Plus Years of Combinatorial Integer Programming.- Reformulation and Decomposition of Integer Programs.- III Current Topics.- Integer Programming and Algorithmic Geometry of Numbers.- Nonlinear Integer Programming.- Mixed Integer Programming Computation.- Symmetry in Integer Linear Programming.- Semidefinite Relaxations for Integer Programming.- The Group-Theoretic Approach in Mixed Integer Programming.

Exact Ground States of Ising Spin Glasses: New Experimental Results With a Branch and Cut Algorithm

In this paper we study two-dimensional Ising spin glasses on a grid with nearest neighbor and periodic boundary interactions, based on a Gaussian bond distribution, and an exterior magnetic field. We show how using a technique called branch and cut, the exact ground states of grids of sizes up to 100×100 can be determined in a moderate amount of computation time, and we report on extensive computational tests. With our method we produce results based on more than 20,000 experiments on the properties of spin glasses whose errors depend only on the assumptions on the model and not on the computational process. This feature is a clear advantage of the method over other, more popular ways to compute the ground state, like Monte Carlo simulation including simulated annealing, evolutionary, and genetic algorithms, that provide only approximate ground states with a degree of accuracy that cannot be determineda priori. Our ground-state energy estimation at zero field is −1.317.

A Branch & Cut Algorithm for the Asymmetric Traveling Salesman Problem with Precedence Constraints

In this article we consider a variant of the classical asymmetric traveling salesman problem (ATSP), namely the ATSP in which precedence constraints require that certain nodes must precede certain other nodes in any feasible directed tour. This problem occurs as a basic model in scheduling and routing and has a wide range of applications varying from helicopter routing (Timlin, Masters Thesis, Department of Combinatorics and Optimization, University of Waterloo, 1989), sequencing in flexible manufacturing (Ascheuer et al., Integer Programming and Combinatorial Optimization, University of Waterloo, Waterloo, 1990, pp. 19–28; Idem., SIAM Journal on Optimization, vol. 3, pp. 25–42, 1993), to stacker crane routing in an automatic storage system (Ascheuer, Ph.D. Thesis, Tech. Univ. Berlin, 1995). We give an integer programming model and summarize known classes of valid inequalities. We describe in detail the implementation of a branch&cut-algorithm and give computational results on real-world instances and benchmark problems from TSPLIB. The results we achieve indicate that our implementation outperforms other implementations found in the literature. Real world instances with more than 200 nodes can be solved to optimality within a few minutes of CPU-time. As a side product we obtain a branch&cut-algorithm for the ATSP. All instances in TSPLIB can be solved to optimality in a reasonable amount of computation time.