White, 1650 [[electronic resource] ] : a new almanack and prognostication for the yeare of our Lord Christ 1650, being the second after bissextile or leap-yeare : wherein is contained varietie of matter worth the observation : calculated for the meridian of the most honourable city of London and will serve generally for the monarchy of Great Britaine / / by John White .

• Record Nr.: UNISA996386256203316
• Autore: White John <fl. 1613-1651.>
• Pubbl/distr/stampa: London, : Printed by F.K. for the Company of Stationers, 1650
• Descrizione fisica: [20] p. : ill., map
• Soggetti: Almanacs, English
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "A prognostication for the year of our Lord Christ 1650 ..." has special t.p.; Reproduction of original in the University of Illinois (Urbana-Champaign Campus). Library.
• Sommario/riassunto: eebo-0167

Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA / / David A. Bader [and three others], editors

• Record Nr.: UNINA9910811759503321
• Pubbl/distr/stampa: Providence, Rhode Island : , : American Mathematical Society, , 2013; ©2013
• ISBN: 0-8218-9869-8
• Descrizione fisica: 1 online resource (258 p.)
• Collana: Contemporary mathematics, , 1098-3627 ; ; 588 , 0271-4132
• Classificazione: 05C8568W0505C8268W1068R1005C5005C65
• Disciplina: 511/.5
• Soggetti: Graph algorithms; Graph theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Preface -- 1. Introducing the 10th Challenge " Graph Partitioning and Graph Clustering -- 2. Key Results -- 3. Challenge Description -- 4. Contributions to this Collection -- 5. Directions for Further Research -- High quality graph partitioning -- 1. Introduction -- 2. Preliminaries -- 3. Related Work -- 4. Karlsruhe Fast Flow Partitioner -- 5. KaFFPa Evolutionary -- 6. Experiments -- 7. Conclusion and Future Work -- References -- Abusing a hypergraph partitioner for unweighted graph partitioning -- 1. Introduction -- 2. Mondriaan -- 3. Results -- 4. Conclusion -- References -- Parallel partitioning with Zoltan: Is hypergraph partitioning worth it? -- 1. Introduction -- 2. Models and Metrics -- 3. Overview of the Zoltan Hypergraph Partitioner -- 4. Experiments -- 5. Conclusions -- Acknowledgements -- References -- UMPa: A multi-objective, multi-level partitioner for communication minimization -- 1. Introduction -- 2. Background -- 3. UMPa: A multi-objective partitioning tool for communication minimization -- 4. Experimental results -- 5. Conclusions and future work -- References -- Appendix A. DIMACS Challenge Results -- Shape optimizing load balancing for MPI-parallel adaptive numerical simulations -- 1. Introduction -- 2. Related Work -- 3. Diffusion-based Repartitioning with DibaP -- 4. PDibaP: Parallel DibaP for Repartitioning -- 5. Experiments -- 6. Conclusions -- ; References -- Graph partitioning for scalable distributed graph computations -- 1. Introduction -- 2. Parallel Breadth-first Search -- 3. Analysis of Communication Costs -- 4. Graph and Hypergraph Partitioning Metrics -- 5. Experimental Setup -- 6. Microbenchmarking Collectives Performance -- 7. Performance Analysis and Results -- 8. Conclusions and Future Work -- Acknowledgments -- References -- Appendix on edge count per processor -- Using graph partitioning for efficient network modularity optimization -- 1. Introduction -- 2. Reduction of modularity optimization to minimum weighted cut -- 3. Implementation of the modularity optimization algorithm based on the Metis package -- 4. Comparison on DIMACS testbed graphs -- 5. Conclusion -- References -- Modularity maximization in networks by variable neighborhood search -- 1. Introduction -- 2. Description of the heuristic -- 3. Description of the exact method -- 4. Experimental Results -- 5. Conclusion -- References -- Network clustering via clique relaxations: A community based approach -- 1. Introduction -- 2. Background -- 3. Clustering Algorithm -- 4. Computational Results -- 5. Conclusion -- Acknowledgements -- References -- Identifying base clusters and their application to maximizing modularity -- Complete hierarchical cut-clustering: A case study on expansion and modularity -- A partitioning-based divisive clustering technique for maximizing the modularity -- An ensemble learning strategy for graph clustering -- Parallel community detection for massive graphs -- Graph coarsening and clustering on the GPU.