DIMACS

The DIMACS Implementation Challenges address questions of determining realistic algorithm performance where worst case analysis is overly pessimistic and probabilistic models are too unrealistic: experimentation can provide guides to realistic algorithm performance where analysis fails. Experimentation also brings algorithmic questions closer to the original problems that motivated theoretical work. It also tests many assumptions about implementation methods and data structures. It provides an opportunity to develop and test problem instances, instance generators, and other methods of testing and comparing performance of algorithms. And it is a step in technology transfer by providing leading edge implementations of algorithms for others to adapt. The information on challenges includes pointers to WWW/FTP sites that include calls for participation, algorithm implementations, instance generators, bibliographies, and other electronic artifacts. The challenge organizers are also producing refereed volumes in the AMS-DIMACS book series; these contain selected papers from the workshops that culminate each challenge. If you are using the implementations, generators or other files, please take a few minutes to tell us how you are using it, what applications you are working on, and how it impacts your work. We need to document the impact of this research to the agencies and foundations that support it - your stories are essential to doing that. Send comments to: froberts@dimacs.rutgers.edu


References in zbMATH (referenced in 346 articles )

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  1. Al-Saedi, Balasim; Fourdrinoy, Olivier; Grégoire, Éric; Mazure, Bertrand; Saïs, Lakhdar: About some UP-based polynomial fragments of SAT (2017)
  2. Balasubramaniam, Chitra; Butenko, Sergiy: On robust clusters of minimum cardinality in networks (2017)
  3. Galán, Severino F.: Simple decentralized graph coloring (2017)
  4. Haus, Utz-Uwe; Michini, Carla: Compact representations of all members of an independence system (2017)
  5. Paes, Aline; Zaverucha, Gerson; Costa, Vítor Santos: On the use of stochastic local search techniques to revise first-order logic theories from examples (2017)
  6. Ras, Charl; Swanepoel, Konrad; Thomas, Doreen Anne: Approximate Euclidean Steiner trees (2017)
  7. Akiba, Takuya; Iwata, Yoichi: Branch-and-reduce exponential/FPT algorithms in practice: a case study of vertex cover (2016)
  8. Balyo, Tomáš; Biere, Armin; Iser, Markus; Sinz, Carsten: SAT race 2015 (2016)
  9. De Santis, M.; Festa, P.; Liuzzi, G.; Lucidi, S.; Rinaldi, F.: A nonmonotone GRASP (2016)
  10. Feldmann, Andreas Emil; Könemann, Jochen; Olver, Neil; Sanità, Laura: On the equivalence of the bidirected and hypergraphic relaxations for Steiner tree (2016)
  11. Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
  12. Furini, Fabio; Gabrel, Virginie; Ternier, Ian-Christopher: Lower bounding techniques for DSATUR-based branch and bound (2016)
  13. Hamadi, Youssef; Jabbour, Saïd; Saïs, Lakhdar: What we can learn from conflicts in propositional satisfiability (2016)
  14. Luz, Carlos J.: A characterization of the weighted Lovász number based on convex quadratic programming (2016)
  15. Meyerhenke, Henning; Sanders, Peter; Schulz, Christian: Partitioning (hierarchically clustered) complex networks via size-constrained graph clustering (2016)
  16. Morrison, David R.; Sewell, Edward C.; Jacobson, Sheldon H.: Solving the pricing problem in a branch-and-price algorithm for graph coloring using zero-suppressed binary decision diagrams (2016)
  17. Napov, Artem; Notay, Yvan: An efficient multigrid method for graph Laplacian systems (2016)
  18. O’Donoghue, Brendan; Chu, Eric; Parikh, Neal; Boyd, Stephen: Conic optimization via operator splitting and homogeneous self-dual embedding (2016)
  19. Osterman, Colin; Rego, César: A $k$-level data structure for large-scale traveling salesman problems (2016)
  20. Pajouh, Foad Mahdavi; Balasundaram, Balabhaskar; Hicks, Illya V.: On the 2-club polytope of graphs (2016)

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