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 503 articles )

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  1. Bettiol, Enrico; Létocart, Lucas; Rinaldi, Francesco; Traversi, Emiliano: A conjugate direction based simplicial decomposition framework for solving a specific class of dense convex quadratic programs (2020)
  2. Calle, F. Javier; Cuadra, Dolores; Rivero, Jesica; Isasi, Pedro: Boosting the exploration of huge dynamic graphs (2020)
  3. Ertem, Zeynep; Lykhovyd, Eugene; Wang, Yiming; Butenko, Sergiy: The maximum independent union of cliques problem: complexity and exact approaches (2020)
  4. Feldmann, Andreas Emil; Marx, Dániel: The parameterized hardness of the (k)-center problem in transportation networks (2020)
  5. Georgiadis, Loukas; Italiano, Giuseppe F.; Karanasiou, Aikaterini: Approximating the smallest 2-vertex connected spanning subgraph of a directed graph (2020)
  6. Goerigk, Marc; Maher, Stephen J.: Generating hard instances for robust combinatorial optimization (2020)
  7. Gouveia, João; Pong, Ting Kei; Saee, Mina: Inner approximating the completely positive cone via the cone of scaled diagonally dominant matrices (2020)
  8. Grodet, Aymeric; Tsuchiya, Takuya: Reorganizing topologies of Steiner trees to accelerate their eliminations (2020)
  9. Maske, Charles; Cohen, Jaime; Duarte, Elias P. jun.: Speeding up the Gomory-Hu parallel cut tree algorithm with efficient graph contractions (2020)
  10. Miasnikof, Pierre; Pitsoulis, Leonidas; Bonner, Anthony J.; Lawryshyn, Yuri; Pardalos, Panos M.: Graph clustering via intra-cluster density maximization (2020)
  11. Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng; Zhao, Xin-Yuan: SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0) (2020)
  12. Wang, Yiyuan; Cai, Shaowei; Chen, Jiejiang; Yin, Minghao: SCCWalk: an efficient local search algorithm and its improvements for maximum weight clique problem (2020)
  13. Zhou, Qing; Benlic, Una; Wu, Qinghua: An opposition-based memetic algorithm for the maximum quasi-clique problem (2020)
  14. Anderson, Matthew; Williamson, Matthew; Subramani, K.: Empirical analysis of algorithms for the shortest negative cost cycle problem (2019)
  15. 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)
  16. Cornaz, Denis; Furini, Fabio; Lacroix, Mathieu; Malaguti, Enrico; Mahjoub, A. Ridha; Martin, Sébastien: The vertex (k)-cut problem (2019)
  17. Džamić, Dušan; Aloise, Daniel; Mladenović, Nenad: Ascent-descent variable neighborhood decomposition search for community detection by modularity maximization (2019)
  18. Feldmann, Andreas Emil: Fixed-parameter approximations for (k)-center problems in low highway dimension graphs (2019)
  19. Festa, P.; Guerriero, F.; Napoletano, A.: An auction-based approach for the re-optimization shortest path tree problem (2019)
  20. Furini, Fabio; Ljubić, Ivana; Martin, Sébastien; San Segundo, Pablo: The maximum clique interdiction problem (2019)

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