Algorithms – ESA 2013: 21st Annual European Symposium, by David Adjiashvili, Gianpaolo Oriolo, Marco Senatore (auth.),

By David Adjiashvili, Gianpaolo Oriolo, Marco Senatore (auth.), Hans L. Bodlaender, Giuseppe F. Italiano (eds.)

This publication constitutes the refereed lawsuits of the twenty first Annual ecu Symposium on Algorithms, ESA 2013, held in Sophia Antipolis, France, in September 2013 within the context of the mixed convention ALGO 2013. The sixty nine revised complete papers awarded have been rigorously reviewed and chosen from 303 preliminary submissions: fifty three out of 229 in tune "Design and research" and sixteen out of seventy four in song "Engineering and Applications". The papers during this booklet current unique study in all parts of algorithmic examine, together with yet no longer restricted to: set of rules engineering; algorithmic features of networks; algorithmic online game conception; approximation algorithms; computational biology; computational finance; computational geometry; combinatorial optimization; information compression; info buildings; databases and data retrieval; dispensed and parallel computing; graph algorithms; hierarchical thoughts; heuristics and meta-heuristics; mathematical programming; cellular computing; online algorithms; parameterized complexity; development matching; quantum computing; randomized algorithms; scheduling and source allocation difficulties; streaming algorithms.

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Additional resources for Algorithms – ESA 2013: 21st Annual European Symposium, Sophia Antipolis, France, September 2-4, 2013. Proceedings

Example text

Thus, the propagation of the prefix sums values can be performed during this copying process. Next, we process the P child slabs in parallel using sequential distribution sweeping. This recursively subdivides the slabs till the pre-specified threshold 2 In our experiments, performing this step sequentially takes less than a millisecond, while the overall running time is in dozens or hundreds of seconds. Empirical Evaluation of the Parallel Distribution Sweeping Framework 31 M is reached. When generating the input lists for the child slabs, we also store the total number of segments and query points for the child slabs.

7. Obtaining T + and Ts from T ∗ Eventually, we show that a flip in T ∗ corresponds to at most one flip either in T + or in precisely one Ts for some sink s. We do this by considering all the possibilities for two triangles that share a common flippable edge. Note that by us construction no two triangles mapped to triangulations of different polygons PD s ut and PDt can share an edge (with t = s being another sink). Case 1. We flip an edge between two triangles that are either both mapped to T + or to Ts and are different from Δs .

Recall our discussion that for the parameters of our systems K = n/M and the I/O complexity of the distribution sweeping algorithm is O((n/B)(1 + logK n/M )) = O(n/B). This explains the non-linear asymptotic speedup over plane sweep algorithm (with I/O complexity of O((n/B) log n/M )) as a function of the input size. Figure 4 shows the speedup that parallel distribution sweeping algorithm achieves relative to the sequential distribution sweeping algorithm. PRAM vs. PEM Performance. Figure 5 (left) shows the comparative performance of the various algorithms on configuration 3.

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