Concorde

Concorde is a computer code for the symmetric traveling salesman problem (TSP) and some related network optimization problems. The code is written in the ANSI C programming language and it is available for academic research use; for other uses, contact William Cook for licensing options. Concorde’s TSP solver has been used to obtain the optimal solutions to 106 of the 110 TSPLIB instances; the largest having 85,900 cities. The Concorde callable library includes over 700 functions permitting users to create specialized codes for TSP-like problems. All Concorde functions are thread-safe for programming in shared-memory parallel environments; the main TSP solver includes code for running over networks of UNIX workstations.


References in zbMATH (referenced in 179 articles , 1 standard article )

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  1. Mohan, Usha; Ramani, Sivaramakrishnan; Mishra, Sounaka: Constant factor approximation algorithm for TSP satisfying a biased triangle inequality (2017)
  2. Sundar, Kaarthik; Rathinam, Sivakumar: Multiple depot ring star problem: a polyhedral study and an exact algorithm (2017)
  3. Vanneschi, Leonardo: An introduction to geometric semantic genetic programming (2017)
  4. Bartal, Yair; Gottlieb, Lee-Ad; Krauthgamer, Robert: The traveling salesman problem: low-dimensionality implies a polynomial time approximation scheme (2016)
  5. Braun, Gábor; Pokutta, Sebastian: A polyhedral characterization of border bases (2016)
  6. Coutinho, Walton Pereira; do Nascimento, Roberto Quirino; Pessoa, Artur Alves; Subramanian, Anand: A branch-and-bound algorithm for the close-enough traveling salesman problem (2016)
  7. Doppstadt, C.; Koberstein, A.; Vigo, D.: The hybrid electric vehicle-traveling salesman problem (2016)
  8. Fages, Jean-Guillaume; Lorca, Xavier; Rousseau, Louis-Martin: The salesman and the tree: the importance of search in CP (2016)
  9. Fischer, Anja: A polyhedral study of the quadratic traveling salesman problem (2016)
  10. Klaučo, Martin; Blažek, Slavomír; Kvasnica, Michal: An optimal path planning problem for heterogeneous multi-vehicle systems (2016)
  11. Lei, Hongtao; Wang, Rui; Laporte, Gilbert: Solving a multi-objective dynamic stochastic districting and routing problem with a co-evolutionary algorithm (2016)
  12. Megow, Nicole; Skutella, Martin; Verschae, José; Wiese, Andreas: The power of recourse for online MST and TSP (2016)
  13. Rendl, F.: Semidefinite relaxations for partitioning, assignment and ordering problems (2016)
  14. Roldán, Raúl F.; Basagoiti, Rosa; Coelho, Leandro C.: Robustness of inventory replenishment and customer selection policies for the dynamic and stochastic inventory-routing problem (2016)
  15. Seth, Anupam; Klabjan, Diego; Ferreira, Placid M.: A new novel local search integer-programming-based heuristic for PCB assembly on collect-and-place machines (2016)
  16. Subramanyam, Anirudh; Gounaris, Chrysanthos E.: A branch-and-cut framework for the consistent traveling salesman problem (2016)
  17. Taccari, Leonardo: Integer programming formulations for the elementary shortest path problem (2016)
  18. Vaksman, Gregory; Zibulevsky, Michael; Elad, Michael: Patch ordering as a regularization for inverse problems in image processing (2016)
  19. Vasilyev, Igor; Boccia, Maurizio; Hanafi, Saïd: An implementation of exact knapsack separation (2016)
  20. Weise, Thomas; Wu, Yuezhong; Chiong, Raymond; Tang, Ke; Lässig, Jörg: Global versus local search: the impact of population sizes on evolutionary algorithm performance (2016)

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