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 445 articles )

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  1. Anderson, Matthew; Williamson, Matthew; Subramani, K.: Empirical analysis of algorithms for the shortest negative cost cycle problem (2019)
  2. Taillard, Éric D.; Helsgaun, Keld: POPMUSIC for the travelling salesman problem (2019)
  3. Di Puglia Pugliese, Luigi; Gaudioso, Manlio; Guerriero, Francesca; Miglionico, Giovanna: A Lagrangean-based decomposition approach for the link constrained Steiner tree problem (2018)
  4. Feldmann, Andreas Emil; Fung, Wai Shing; Könemann, Jochen; Post, Ian: A $(1+\varepsilon)$-embedding of low highway dimension graphs into bounded treewidth graphs (2018)
  5. Gonzaga de Oliveira, Sanderson L.; Bernardes, J. A. B.; Chagas, G. O.: An evaluation of reordering algorithms to reduce the computational cost of the incomplete Cholesky-conjugate gradient method (2018)
  6. Gonzaga de Oliveira, Sanderson L.; Bernardes, Júnior A. B.; Chagas, Guilherme O.: An evaluation of low-cost heuristics for matrix bandwidth and profile reductions (2018)
  7. Kokosiński, Zbigniew; Bała, Marcin: Solving graph partitioning problems with parallel metaheuristics (2018)
  8. Lu, Yajun; Moradi, Esmaeel; Balasundaram, Balabhaskar: Correction to: “Finding a maximum $k$-club using the $k$-clique formulation and canonical hypercube cuts” (2018)
  9. Marques-Silva, Joao; Malik, Sharad: Propositional SAT solving (2018)
  10. Moradi, Esmaeel; Balasundaram, Balabhaskar: Finding a maximum $k$-club using the $k$-clique formulation and canonical hypercube cuts (2018)
  11. Orden, David; Gimenez-Guzman, Jose Manuel; Marsa-Maestre, Ivan; de la Hoz, Enrique: Spectrum graph coloring and applications to Wi-Fi channel assignment (2018)
  12. Pajor, Thomas; Uchoa, Eduardo; Werneck, Renato F.: A robust and scalable algorithm for the Steiner problem in graphs (2018)
  13. Pandit, Parthe; Kulkarni, Ankur A.: A linear complementarity based characterization of the weighted independence number and the independent domination number in graphs (2018)
  14. Permenter, Frank; Parrilo, Pablo: Partial facial reduction: simplified, equivalent SDPs via approximations of the PSD cone (2018)
  15. Pinto, Bruno Q.; Ribeiro, Celso C.; Rosseti, Isabel; Plastino, Alexandre: A biased random-key genetic algorithm for the maximum quasi-clique problem (2018)
  16. Rigó, Petra Renáta; Darvay, Zsolt: Infeasible interior-point method for symmetric optimization using a positive-asymptotic barrier (2018)
  17. Roux, Pierre; Voronin, Yuen-Lam; Sankaranarayanan, Sriram: Validating numerical semidefinite programming solvers for polynomial invariants (2018)
  18. San Segundo, Pablo; Artieda, Jorge; Strash, Darren: Efficiently enumerating all maximal cliques with bit-parallelism (2018)
  19. Stidsen, Thomas; Andersen, Kim Allan: A hybrid approach for biobjective optimization (2018)
  20. Yezerska, Oleksandra; Butenko, Sergiy; Boginski, Vladimir L.: Detecting robust cliques in graphs subject to uncertain edge failures (2018)

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