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 515 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. Furini, Fabio; Ljubić, Ivana; Malaguti, Enrico; Paronuzzi, Paolo: On integer and bilevel formulations for the (k)-vertex cut problem (2020)
  6. Gaar, Elisabeth; Rendl, Franz: A computational study of exact subgraph based SDP bounds for max-cut, stable set and coloring (2020)
  7. Georgiadis, Loukas; Italiano, Giuseppe F.; Karanasiou, Aikaterini: Approximating the smallest 2-vertex connected spanning subgraph of a directed graph (2020)
  8. Goerigk, Marc; Maher, Stephen J.: Generating hard instances for robust combinatorial optimization (2020)
  9. Gouveia, João; Pong, Ting Kei; Saee, Mina: Inner approximating the completely positive cone via the cone of scaled diagonally dominant matrices (2020)
  10. Grodet, Aymeric; Tsuchiya, Takuya: Reorganizing topologies of Steiner trees to accelerate their eliminations (2020)
  11. Ibrahim, Mohamed-Hamza; Pal, Christopher; Pesant, Gilles: Leveraging cluster backbones for improving MAP inference in statistical relational models (2020)
  12. Manuel, Cassius; von Haeseler, Arndt: Structure of the space of taboo-free sequences (2020)
  13. Maske, Charles; Cohen, Jaime; Duarte, Elias P. jun.: Speeding up the Gomory-Hu parallel cut tree algorithm with efficient graph contractions (2020)
  14. Miasnikof, Pierre; Pitsoulis, Leonidas; Bonner, Anthony J.; Lawryshyn, Yuri; Pardalos, Panos M.: Graph clustering via intra-cluster density maximization (2020)
  15. Shimizu, Satoshi; Yamaguchi, Kazuaki; Masuda, Sumio: A maximum edge-weight clique extraction algorithm based on branch-and-bound (2020)
  16. Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng; Zhao, Xin-Yuan: SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0) (2020)
  17. Wang, Yiyuan; Cai, Shaowei; Chen, Jiejiang; Yin, Minghao: SCCWalk: an efficient local search algorithm and its improvements for maximum weight clique problem (2020)
  18. Zhou, Qing; Benlic, Una; Wu, Qinghua: An opposition-based memetic algorithm for the maximum quasi-clique problem (2020)
  19. Anderson, Matthew; Williamson, Matthew; Subramani, K.: Empirical analysis of algorithms for the shortest negative cost cycle problem (2019)
  20. 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)

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