Showing papers by "Guido Schäfer published in 2002"
TL;DR: The implementation of an algorithm which solves the weighted matching problem in general graphs with n vertices and m edges in time O(nm log n) is described, which is a variant of the algorithm of Galil, Micali and Gabow and extensively uses sophisticated data structures.
Abstract: We describe the implementation of an algorithm which solves the weighted matching problem in general graphs with n vertices and m edges in time O(nm log n). Our algorithm is a variant of the algorithm of Galil, Micali and Gabow [Galil et al. 1986] and extensively uses sophisticated data structures, in particular concatenable priority queues, so as to reduce the time needed to perform dual adjustments and to find tight edges in Edmonds' blossom-shrinking algorithm.We compare our implementation to the experimentally fastest implementation, named Blossom IV, due to Cook and Rohe [Cook and Rohe 1997]. Blossom IV requires only very simple data structures and has an asymptotic running time of O(n2m). Our experiments show that our new implementation is superior to Blossom IV. A closer inspection reveals that the running time of Edmonds' blossom-shrinking algorithm in practice heavily depends on the time spent to perform dual adjustments and to find tight edges. Therefore, optimizing these operations, as is done in our implementation, indeed speeds-up the practical performance of implementations of Edmonds' algorithm.
40 citations
01 Jan 2002
TL;DR: The tool set is designed not only to make computational experimentation easier but also to support good scientific practice by making results reproducable and more easily comparable to others’ results by automatically documenting the experimental environment.
Abstract: We describe a set of tools that support the running, documentation, and evaluation of computational experiments. The tool set is designed not only to make computational experimentation easier but also to support good scientific practice by making results reproducable and more easily comparable to others’ results by automatically documenting the experimental environment. The tools can be used separately or in concert and support all manner of experiments (i.e., any executable can be an experiment). The tools capitalize on the rich functionality available in Python to provide extreme flexibility and ease of use, but one need know nothing of Python to use the tools.
17 citations
TL;DR: An algorithm that solves the all-pairs shortest-paths problem on a directed graph with n vertices and m arcs in time O(nm+n2logn), where the arcs are assigned real, possibly negative costs.
7 citations