Euclidean optimization problems such as TSP and minimum-length matching admit fast partitioning algorithms that compute near-optimal solutions on typical instances.
We develop a general framework for the application of smoothed analysis to partitioning algorithms for Euclidean optimization problems. Our framework can be used to analyze both the running-time and the approximation ratio of such algorithms. We apply our framework to obtain smoothed analyses of Dyer and Frieze's partitioning algorithm for Euclidean matching, Karp's partitioning scheme for the TSP, a heuristic for Steiner trees, and a heuristic for degree-bounded minimum-length spanning trees.
|Name||Lecture Notes in Computer Science|
|Conference||12th International Symposium on Algorithms and Data Structures, WADS 2011|
|Period||15/08/11 → 17/08/11|
|Other||15-17 Aug 2011|