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 547 articles )

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  1. Bomze, Immanuel M.; Kahr, Michael; Leitner, Markus: Trust your data or not -- StQP remains StQP: community detection via robust standard quadratic optimization (2021)
  2. Castro, Jordi; Nasini, Stefano: A specialized interior-point algorithm for huge minimum convex cost flows in bipartite networks (2021)
  3. Chen, Liang; Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming (2021)
  4. Chen, Xiaoyu; Zhou, Yi; Hao, Jin-Kao; Xiao, Mingyu: Computing maximum (k)-defective cliques in massive graphs (2021)
  5. Disser, Yann; Feldmann, Andreas Emil; Klimm, Max; Könemann, Jochen: Travelling on graphs with small highway dimension (2021)
  6. Furini, Fabio; Ljubić, Ivana; San Segundo, Pablo; Zhao, Yanlu: A branch-and-cut algorithm for the edge interdiction clique problem (2021)
  7. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; Tušar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  8. Hu, Yue; Harabor, Daniel; Qin, Long; Yin, Quanjun: Regarding goal bounding and jump point search (2021)
  9. Turkeš, Renata; Sörensen, Kenneth; Hvattum, Lars Magnus: Meta-analysis of metaheuristics: quantifying the effect of adaptiveness in adaptive large neighborhood search (2021)
  10. Yurtsever, Alp; Tropp, Joel A.; Fercoq, Olivier; Udell, Madeleine; Cevher, Volkan: Scalable semidefinite programming (2021)
  11. 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)
  12. Bombina, Polina; Ames, Brendan: Convex optimization for the densest subgraph and densest submatrix problems (2020)
  13. Calle, F. Javier; Cuadra, Dolores; Rivero, Jesica; Isasi, Pedro: Boosting the exploration of huge dynamic graphs (2020)
  14. Ertem, Zeynep; Lykhovyd, Eugene; Wang, Yiming; Butenko, Sergiy: The maximum independent union of cliques problem: complexity and exact approaches (2020)
  15. Feldmann, Andreas Emil; Marx, Dániel: The parameterized hardness of the (k)-center problem in transportation networks (2020)
  16. Furini, Fabio; Ljubić, Ivana; Malaguti, Enrico; Paronuzzi, Paolo: On integer and bilevel formulations for the (k)-vertex cut problem (2020)
  17. Gaar, Elisabeth; Rendl, Franz: A computational study of exact subgraph based SDP bounds for max-cut, stable set and coloring (2020)
  18. Georgiadis, Loukas; Italiano, Giuseppe F.; Karanasiou, Aikaterini: Approximating the smallest 2-vertex connected spanning subgraph of a directed graph (2020)
  19. Goerigk, Marc; Maher, Stephen J.: Generating hard instances for robust combinatorial optimization (2020)
  20. Gouveia, João; Pong, Ting Kei; Saee, Mina: Inner approximating the completely positive cone via the cone of scaled diagonally dominant matrices (2020)

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