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 571 articles )

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  1. Accorsi, Luca; Lodi, Andrea; Vigo, Daniele: Guidelines for the computational testing of machine learning approaches to vehicle routing problems (2022)
  2. Balasundaram, Balabhaskar; Borrero, Juan S.; Pan, Hao: Graph signatures: identification and optimization (2022)
  3. de Souza, Marcelo; Ritt, Marcus; López-Ibáñez, Manuel: Capping methods for the automatic configuration of optimization algorithms (2022)
  4. Fampa, Marcia: Insight into the computation of Steiner minimal trees in Euclidean space of general dimension (2022)
  5. Nascimento Silva, Janio Carlos; Coelho, Igor M.; Souza, Ueverton S.; Ochi, Luiz Satoru; Coelho, Vitor N.: Finding the maximum multi improvement on neighborhood exploration (2022)
  6. San Segundo, Pablo; Furini, Fabio; León, Rafael: A new branch-and-filter exact algorithm for binary constraint satisfaction problems (2022)
  7. Swan, Jerry; Adriaensen, Steven; Brownlee, Alexander E. I.; Hammond, Kevin; Johnson, Colin G.; Kheiri, Ahmed; Krawiec, Faustyna; Merelo, J. J.; Minku, Leandro L.; Özcan, Ender; Pappa, Gisele L.; García-Sánchez, Pablo; Sörensen, Kenneth; Voß, Stefan; Wagner, Markus; White, David R.: Metaheuristics “In the large” (2022)
  8. Vera, Alberto; Banerjee, Siddhartha; Samaranayake, Samitha: Computing constrained shortest-paths at scale (2022)
  9. Veremyev, Alexander; Boginski, Vladimir; Pasiliao, Eduardo L.; Prokopyev, Oleg A.: On integer programming models for the maximum 2-club problem and its robust generalizations in sparse graphs (2022)
  10. Zhou, Yi; Lin, Weibo; Hao, Jin-Kao; Xiao, Mingyu; Jin, Yan: An effective branch-and-bound algorithm for the maximum (s)-bundle problem (2022)
  11. Badri, Hossein; Bahreini, Tayebeh; Grosu, Daniel: A parallel randomized approximation algorithm for non-preemptive single machine scheduling with release dates and delivery times (2021)
  12. Bomze, Immanuel M.; Kahr, Michael; Leitner, Markus: Trust your data or not -- StQP remains StQP: community detection via robust standard quadratic optimization (2021)
  13. Castro, Jordi; Nasini, Stefano: A specialized interior-point algorithm for huge minimum convex cost flows in bipartite networks (2021)
  14. Cerulli, Martina; De Santis, Marianna; Gaar, Elisabeth; Wiegele, Angelika: Improving ADMMs for solving doubly nonnegative programs through dual factorization (2021)
  15. Chatterjee, Krishnendu; Ibsen-Jensen, Rasmus; Pavlogiannis, Andreas: Faster algorithms for quantitative verification in bounded treewidth graphs (2021)
  16. 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)
  17. Chen, Xiaoyu; Zhou, Yi; Hao, Jin-Kao; Xiao, Mingyu: Computing maximum (k)-defective cliques in massive graphs (2021)
  18. Ding, Lijun; Yurtsever, Alp; Cevher, Volkan; Tropp, Joel A.; Udell, Madeleine: An optimal-storage approach to semidefinite programming using approximate complementarity (2021)
  19. Disser, Yann; Feldmann, Andreas Emil; Klimm, Max; Könemann, Jochen: Travelling on graphs with small highway dimension (2021)
  20. Furini, Fabio; Ljubić, Ivana; San Segundo, Pablo; Zhao, Yanlu: A branch-and-cut algorithm for the edge interdiction clique problem (2021)

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