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 483 articles )

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  1. Goerigk, Marc; Maher, Stephen J.: Generating hard instances for robust combinatorial optimization (2020)
  2. Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng; Zhao, Xin-Yuan: SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0) (2020)
  3. Anderson, Matthew; Williamson, Matthew; Subramani, K.: Empirical analysis of algorithms for the shortest negative cost cycle problem (2019)
  4. Asadi, Soodabeh; Mansouri, Hossein; Darvay, Zsolt; Zangiabadi, Maryam; Mahdavi-Amiri, Nezam: Large-neighborhood infeasible predictor-corrector algorithm for horizontal linear complementarity problems over Cartesian product of symmetric cones (2019)
  5. Cornaz, Denis; Furini, Fabio; Lacroix, Mathieu; Malaguti, Enrico; Mahjoub, A. Ridha; Martin, Sébastien: The vertex (k)-cut problem (2019)
  6. Džamić, Dušan; Aloise, Daniel; Mladenović, Nenad: Ascent-descent variable neighborhood decomposition search for community detection by modularity maximization (2019)
  7. Feldmann, Andreas Emil: Fixed-parameter approximations for (k)-center problems in low highway dimension graphs (2019)
  8. Furini, Fabio; Ljubić, Ivana; Martin, Sébastien; San Segundo, Pablo: The maximum clique interdiction problem (2019)
  9. Hosseinian, Seyedmohammadhossein; Butenko, Sergiy: Algorithms for the generalized independent set problem based on a quadratic optimization approach (2019)
  10. Jabrayilov, Adalat; Mutzel, Petra: A new integer linear program for the Steiner tree problem with revenues, budget and hop constraints (2019)
  11. Komusiewicz, Christian; Nichterlein, André; Niedermeier, Rolf; Picker, Marten: Exact algorithms for finding well-connected 2-clubs in sparse real-world graphs: theory and experiments (2019)
  12. Lammich, Peter; Sefidgar, S. Reza: Formalizing network flow algorithms: a refinement approach in Isabelle/HOL (2019)
  13. Lin, Weibo; Xiao, Mingyu; Zhou, Yi; Guo, Zhenyu: Computing lower bounds for minimum sum coloring and optimum cost chromatic partition (2019)
  14. Matsypura, Dmytro; Veremyev, Alexander; Prokopyev, Oleg A.; Pasiliao, Eduardo L.: On exact solution approaches for the longest induced path problem (2019)
  15. Parmentier, Axel: Algorithms for non-linear and stochastic resource constrained shortest path (2019)
  16. San Segundo, Pablo; Coniglio, Stefano; Furini, Fabio; Ljubić, Ivana: A new branch-and-bound algorithm for the maximum edge-weighted clique problem (2019)
  17. San Segundo, Pablo; Furini, Fabio; Artieda, Jorge: A new branch-and-bound algorithm for the maximum weighted clique problem (2019)
  18. Sedeño-noda, Antonio; Colebrook, Marcos: A biobjective Dijkstra algorithm (2019)
  19. Taillard, Éric D.; Helsgaun, Keld: POPMUSIC for the travelling salesman problem (2019)
  20. Tamaki, Hisao: Positive-instance driven dynamic programming for treewidth (2019)

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