QAPLIB

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 221 articles , 2 standard articles )

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  1. Baioletti, Marco; Di Bari, Gabriele; Milani, Alfredo; Santucci, Valentino: An experimental comparison of algebraic crossover operators for permutation problems (2020)
  2. Dantas, Augusto; Pozo, Aurora: On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem (2020)
  3. Kim, Sunyoung; Kojima, Masakazu; Toh, Kim-Chuan: A geometrical analysis on convex conic reformulations of quadratic and polynomial optimization problems (2020)
  4. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (2020)
  5. Lobo, Fernando G.; Bazargani, Mosab; Burke, Edmund K.: A cutoff time strategy based on the coupon collector’s problem (2020)
  6. Sun, Defeng; Toh, Kim-Chuan; Yuan, Yancheng; Zhao, Xin-Yuan: SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0) (2020)
  7. Bomze, Immanuel M.; Cheng, Jianqiang; Dickinson, Peter J. C.; Lisser, Abdel; Liu, Jia: Notoriously hard (mixed-)binary QPs: empirical evidence on new completely positive approaches (2019)
  8. Date, Ketan; Nagi, Rakesh: Level 2 reformulation linearization technique-based parallel algorithms for solving large quadratic assignment problems on graphics processing unit clusters (2019)
  9. Franzin, Alberto; Stützle, Thomas: Revisiting simulated annealing: a component-based analysis (2019)
  10. Ito, Naoki; Kim, Sunyoung; Kojima, Masakazu; Takeda, Akiko; Toh, Kim-Chuan: Algorithm 996: BBCPOP: a sparse doubly nonnegative relaxation of polynomial optimization problems with binary, box, and complementarity constraints (2019)
  11. Molnár-Szipai, Richárd; Varga, Anita: Integrating combinatorial algorithms into a linear programming solver (2019)
  12. Ng, Kien Ming; Tran, Trung Hieu: A parallel water flow algorithm with local search for solving the quadratic assignment problem (2019)
  13. Alsheddy, Abdullah: A penalty-based multi-objectivization approach for single objective optimization (2018)
  14. Buchheim, Christoph; Traversi, Emiliano: Quadratic combinatorial optimization using separable underestimators (2018)
  15. Delorme, Maxence; Iori, Manuel; Martello, Silvano: BPPLIB: a library for bin packing and cutting stock problems (2018)
  16. Ferreira, José F. S. Bravo; Khoo, Yuehaw; Singer, Amit: Semidefinite programming approach for the quadratic assignment problem with a sparse graph (2018)
  17. Hu, Hao; Sotirov, Renata: Special cases of the quadratic shortest path problem (2018)
  18. Ito, N.; Kim, Sunyoung; Kojima, M.; Takeda, A.; Toh, K.-C.: Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems (2018)
  19. Kukal, Jaromír; Mojzeš, Matej: Quantile and mean value measures of search process complexity (2018)
  20. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: QSDPNAL: a two-phase augmented Lagrangian method for convex quadratic semidefinite programming (2018)

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Further publications can be found at: https://www.opt.math.tugraz.at/qaplib/refs.html