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3 results for “partitioned algorithm”
Figure 6. One chromosome from the population and the five chromosomes existing in the evaluation partition.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>For example comparing the<br> chromosome given in Figure 6 with the first chromosome in the evaluation partition, the<br> difference between the relation Med-Med and Med-High as a pair is 0.0 and the difference<br> between the relation High-High and High-Med as a pair is 0.1. Similarly the difference between<br> the relation Med-Cold and Med-Hot as a pair is 0.1 and the difference between the relation<br> High-Cold and High-Hot as a pair is 0.2. We sum all these differences to get the value of<br> compare(i,j), the sum value is 0+0.1+0.1+0.2 = 0.4. Using the same approach we compute the<br> compare function with the other four chromosomes and we get values 0.4, 0.5,0.4 and 0.6. Now<br> we sum the five values 0.4 + 0.4 + 0.5+ 0.4 +0.6 = 2.3. The fitness value is then 1/ 2.3 = 0.434.<br> The highest is the fitness value, the better is the performance of the chromosome.</p>
A Benchmark Set for Multilevel Hypergraph Partitioning Algorithms
<p>DESCRIPTION<br> -------------------------------------------------------------------------------------------------------<br> This archive contains a large benchmark set for hypergraph partitioning algorithms.<br> All hypergraphs are unweighted (i.e., have unit edge and vertex weights) and use<br> the hMetis hypergraph input file format [1].</p> <p>BENCHMARK SETS<br> -------------------------------------------------------------------------------------------------------<br> Hypergraphs are derived from the following benchmark sets:<br> - The ISPD98 Circuit Benchmark Suite [2]<br> - The DAC 2012 Routability-Driven Placement Contest [3]<br> - The international SAT Competition 2014 [4]<br> - The University of Florida Sparse Matrix Collection (UF-SPM) [5]</p> <p>The benchmark set contains all ISPD98 and DAC2012 instances. Furthermore,<br> it contains 92 randomly selected instances from the application track of the SAT Competition 2014.<br> The Sparse Matrix Collection is organized into 172 groups and each group contains<br> matrices of different application areas. From each group, we chose one matrix for each application <br> area that has between 10 000 and 10.000.000 columns. In case multiple matrices fulfill<br> our criteria, we randomly selected one. In total, we include 192 matrices.</p> <p><br> HYPERGRAPH REPRESENTATION<br> -------------------------------------------------------------------------------------------------------<br> VLSI instances [2,3] are transformed into hypergraphs by converting the netlist into a<br> set of hyperedges. Sparse Matrices are translated into hypergraphs using the row-net model [6],<br> i.e. each row is treated as a net and each column as a vertex. SAT instances are converted into<br> three different hypergraph representations: In the literal model, each boolean literal is mapped to one<br> vertex and each clause constitutes a net [7]. In the primal model each variable is represented by a vertex<br> and each clause is represented by a net, whereas in the dual model the opposite is the case [8].</p> <p>FILE NAMES<br> -------------------------------------------------------------------------------------------------------<br> The origin of each hypergraph (and for SAT instances the hypergraph model) is encoded<br> into the file names as follows:<br> - Sparse Matrices : *.mtx.hgr<br> - DAC2012 : dac2012_superblue*.hgr<br> - ISPD98 : ISPD98_ibm*.hgr<br> - SAT-14 primal : sat14_*.cnf.primal.hgr<br> - SAT-14 dual : sat14_*.cnf.dual.hgr<br> - SAT-14 literal : sat14_*.cnf.hgr</p> <p>REFERENCES<br> -------------------------------------------------------------------------------------------------------<br> [1] http://glaros.dtc.umn.edu/gkhome/fetch/sw/hmetis/manual.pdf<br> [2] C. J. Alpert. The ISPD98 Circuit Benchmark Suite. In Proc. of the 1998 Int. Symp. on Physical Design, pages 80–85, New York, 1998. ACM.<br> [3] N. Viswanathan, C. Alpert, C. Sze, Z. Li, and Y/ Wei. The dac 2012 routability-driven placement contest and benchmark suite. In Proceedings of the 49th Annual Design Automation Conference, DAC ’12, pages 774–782<br> [4] A. Belov, D. Diepold, M. Heule, and M. Järvisalo. The SAT Competition 2014. http://www.satcompetition.org/2014/, 2014.<br> [5] T. A. Davis and Y. Hu. The University of Florida Sparse Matrix Collection. ACM Trans. Math. Softw.,38(1):1:1–1:25, 2011.<br> [6] Ü. V. Catalyürek and C. Aykanat. Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication. IEEE Transactions on Parallel and Distributed Systems, 10(7):673–693, Jul 1999.<br> [7] D. A. Papa and I. L. Markov. Hypergraph Partitioning and Clustering. In T. F. Gonzalez, editor, Handbook of Approximation Algorithms and Metaheuristics. Chapman and Hall/CRC, 2007.<br> [8] Zoltan Mann and Pal Papp. Formula partitioning revisited. In Daniel Le Berre, editor, POS-14. Fifth Pragmatics of SAT workshop, volume 27 of EPiC Series in Computing, pages 41–56. EasyChair, 2014.</p>
Data Set "Systematic partitioning of proteins for quantum-chemical fragmentation methods using graph algorithms"
<p>Data set accompanying the publication "Systematic partitioning of proteins for quantum-chemical fragmentation methods using graph algorithms"</p> <p>The data set contains:</p> <p>- Input script for PyADF (v0.97) for calculating (a) all two body terms to use as graph weights and (b) fragmentation error for all k and nmax (aspf)</p> <p>- PDB files of proteins and the "regions of interest" (RoI) used in this work.</p> <p>- Raw data: protein graph representations, resulting partitions, data underlying all figures shown in our article.</p> <p>- Jupiter notebook to create all figures shown in the article and in the supporting information from data in the results folder.</p> <p>- Images of protein structures and graph representations of ubiquitin.</p>
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