Proc. 10th workshop on algorithm engineering, 5th workshop by J. Ian Munro, Robert Sedgewick, Dorothea Wagner, Wojciech
By J. Ian Munro, Robert Sedgewick, Dorothea Wagner, Wojciech Szpankowski
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Extra resources for Proc. 10th workshop on algorithm engineering, 5th workshop on analytic algorithmics and combinatorics
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Wj3 ) fullfills all claims of the lemma for iteration step i+1 where ulk , ulk +1 is the first edge on P that has been deleted in the pruning step. 1 holds during all phases of all iteration steps of SHARC-preprocessing. So, the preprocessing algorithm (without the refinement phase) is correct. 26 Obtaining Optimal k-Cardinality Trees Fast Markus Chimani∗ Maria Kandyba∗† Abstract Given an undirected graph G = (V, E) with edge weights and a positive integer number k, the k-Cardinality Tree problem consists of finding a subtree T of G with exactly k edges and the minimum possible weight.
Running SHARC on the applied metric. 2 Timetable Information Networks. 51 multi for timetable information. 41 slow car dependent and time-expanded networks (cf. 50 multi details). 37 single fast car obtained by running a shortest path query. 49 Note that we use a logarithmic scale due to outliers. 2 ms. However, for the latter, query times increase up to ranks of 213 which is roughly the size of cells at the lowest level. Above this rank query times decrease and increase again till the size of cells at level 1 is reached.
130 to the 13 categories. Finally, results are given for multi-metric SHARC, which stores only one arc-flag for each edge. As expected, SHARC performs very well on other metrics based on travel times. Stunningly, the loss in performance is only very little when storing only one arc-flag for all three metrics. However, the overhead Table 2: Performance of SHARC on different metrics increases due to storing more edge weights for shortcuts using the European road instance. Multi-metric refers and the size of the arc-flags vector increases slightly.