Experimental algorithms

InhaltsverzeichnisSession 1.Algorithms for Wireless Sensor Networks: Design, Analysis and Experimental Evaluation.Numerical Estimation of the Impact of Interferences on the Localization Problem in Sensor Networks.Session 2.An Efficient Heuristic for the Ring Star Problem.An Incremental Model for Combinatorial Maximization Problems.Workload Balancing in Multi-stage Production Processes.Session 3.Fault Cryptanalysis and the Shrinking Generator.Some Advances in the Theory of Voting Systems Based on Experimental Algorithms.Session 4.Practical Construction of k-Nearest Neighbor Graphs in Metric Spaces.Fast and Simple Approximation of the Diameter and Radius of a Graph.Session 5.Lists on Lists: A Framework for Self-organizing Lists in Environments with Locality of Reference.Lists Revisited: Cache Conscious STL Lists.Engineering the LOUDS Succinct Tree Representation.Session 6.Faster Adaptive Set Intersections for Text Searching.Compressed Dictionaries: Space Measures, Data Sets, and Experiments.Efficient Bit-Parallel Algorithms for (?,?)-Matching.Session 7.Tiny Experiments for Algorithms and Life.Evaluation of Online Strategies for Reordering Buffers.Session 8.Scheduling Unrelated Parallel Machines Computational Results.Implementation of Approximation Algorithms for the Max-Min Resource Sharing Problem.Column Generation Based Heuristic for a Helicopter Routing Problem.Session 9.Kernels for the Vertex Cover Problem on the Preferred Attachment Model.Practical Partitioning-Based Methods for the Steiner Problem.Session 10.Algorithmic and Complexity Results for Decompositions of Biological Networks into Monotone Subsystems.A Maximum Profit Coverage Algorithm with Application to Small Molecules Cluster Identification.Session 11.Algorithmic Challenges in Web Search Engines.On the Least Cost for Proximity Searching in Metric Spaces.Session 12.Updating Directed Minimum Cost Spanning Trees.Experiments on Exact Crossing Minimization Using Column Generation.Goal Directed Shortest Path Queries Using Precomputed Cluster Distances.