A collection of electronically available data instances for the quadratic assignment problem is described. For each instance, we provide detailed information, indicating whether or not the problem is solved to optimality. If not, we supply the best known bounds for the problem. Moreover we survey available software and describe recent dissertations related to the quadratic assignment problem. The paper is an updated version of a previous paper of the authors [Eur. J. Oper. Res. 55, No. 1, 115--119 (1991)].

References in zbMATH (referenced in 190 articles , 2 standard articles )

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  1. Arima, Naohiko; Kim, Sunyoung; Kojima, Masakazu; Toh, Kim-Chuan: A robust Lagrangian-DNN method for a class of quadratic optimization problems (2017)
  2. Xudong Li, Defeng Sun, Kim-Chuan Toh: On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (2017) arXiv
  3. Ahmed, Zakir Hussain: Experimental analysis of crossover and mutation operators on the quadratic assignment problem (2016)
  4. De Santis, M.; Festa, P.; Liuzzi, G.; Lucidi, S.; Rinaldi, F.: A nonmonotone GRASP (2016)
  5. Elloumi, Sourour; Lambert, Amélie: Comparison of quadratic convex reformulations to solve the Quadratic Assignment problem (2016)
  6. Jiang, Bo; Liu, Ya-Feng; Wen, Zaiwen: $L_p$-norm regularization algorithms for optimization over permutation matrices (2016)
  7. John, Maximilian; Karrenbauer, Andreas: A novel SDP relaxation for the quadratic assignment problem using cut pseudo bases (2016)
  8. Lalla-Ruiz, Eduardo; Expósito-Izquierdo, Christopher; Melián-Batista, Belén; Moreno-Vega, J.Marcos: A hybrid biased random key genetic algorithm for the quadratic assignment problem (2016)
  9. Sun, Defeng; Toh, Kim-Chuan; Yang, Liuqin: An efficient inexact ABCD method for least squares semidefinite programming (2016)
  10. Zhang, Huizhen; Li, Qian; Cesar, Beltran-Royo: A new solution method based on Lagrangian relaxation for the quadratic assignment problem (2016)
  11. Ahmed, Zakir Hussain: A multi-parent genetic algorithm for the quadratic assignment problem (2015)
  12. de Klerk, E.; Sotirov, R.; Truetsch, U.: A new semidefinite programming relaxation for the quadratic assignment problem and its computational perspectives (2015)
  13. Drugan, Mădălina M.: Generating QAP instances with known optimum solution and additively decomposable cost function (2015)
  14. Gueye, Serigne; Michelon, Philippe: A linear formulation with $O(n^2)$ variables for quadratic assignment problems with Manhattan distance matrices (2015)
  15. Pardo, Eduardo G.; Soto, Mauricio; Thraves, Christopher: Embedding signed graphs in the line (2015)
  16. Peng, Jiming; Zhu, Tao; Luo, Hezhi; Toh, Kim-Chuan: Semi-definite programming relaxation of quadratic assignment problems based on nonredundant matrix splitting (2015)
  17. Sun, Defeng; Toh, Kim-Chuan; Yang, Liuqin: A convergent 3-block semiproximal alternating direction method of multipliers for conic programming with 4-type constraints (2015)
  18. Xia, Yong; Gharibi, Wajeb: On improving convex quadratic programming relaxation for the quadratic assignment problem (2015)
  19. Yang, Liuqin; Sun, Defeng; Toh, Kim-Chuan: SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints (2015)
  20. Adams, Warren; Waddell, Lucas: Linear programming insights into solvable cases of the quadratic assignment problem (2014)

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