Biq Mac Library - Binary quadratic and Max cut Library. This site offers a collection of Max-Cut instances and quadratic 0-1 programming problems of medium size. Most of the instances were collected while developing Biq Mac, an SDP based Branch & Bound code (see [RRW07] or [Wie06]). The dimension of the problems (i.e., number of variables or number of vertices in the graph) ranges from 20 to 500. The instances are mainly ment to be used for testing exact solution methods for quadratic 0-1 programming or Max-Cut problems.

References in zbMATH (referenced in 23 articles )

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  1. de Souza, Marcelo; Ritt, Marcus; López-Ibáñez, Manuel: Capping methods for the automatic configuration of optimization algorithms (2022)
  2. Bertsimas, Dimitris; Cory-Wright, Ryan; Pauphilet, Jean: A unified approach to mixed-integer optimization problems with logical constraints (2021)
  3. 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)
  4. Hrga, Timotej; Lužar, Borut; Povh, Janez; Wiegele, Angelika: BiqBin: moving boundaries for NP-hard problems by HPC (2021)
  5. Hrga, Timotej; Povh, Janez: \textttMADAM: a parallel exact solver for max-cut based on semidefinite programming and ADMM (2021)
  6. Kim, Sunyoung; Kojima, Masakazu; Toh, Kim-Chuan: A Newton-bracketing method for a simple conic optimization problem (2021)
  7. Nguyen, Viet Hung; Minoux, Michel: Linear size MIP formulation of max-cut: new properties, links with cycle inequalities and computational results (2021)
  8. Zhang, Junyu; Ma, Shiqian; Zhang, Shuzhong: Primal-dual optimization algorithms over Riemannian manifolds: an iteration complexity analysis (2020)
  9. Furini, Fabio; Traversi, Emiliano: Theoretical and computational study of several linearisation techniques for binary quadratic problems (2019)
  10. Rodrigues de Sousa, Vilmar Jefté; Anjos, Miguel F.; Le Digabel, Sébastien: Improving the linear relaxation of maximum (k)-cut with semidefinite-based constraints (2019)
  11. Rodrigues de Sousa, Vilmar Jefté; Anjos, Miguel F.; Le Digabel, Sébastien: Computational study of valid inequalities for the maximum (k)-cut problem (2018)
  12. Arima, Naohiko; Kim, Sunyoung; Kojima, Masakazu; Toh, Kim-Chuan: A robust Lagrangian-DNN method for a class of quadratic optimization problems (2017)
  13. Zhou, Jing; Fang, Shu-Cherng; Xing, Wenxun: Conic approximation to quadratic optimization with linear complementarity constraints (2017)
  14. Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
  15. Kim, Sunyoung; Kojima, Masakazu; Toh, Kim-Chuan: A Lagrangian-DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems (2016)
  16. Maehara, Takanori; Murota, Kazuo: Valuated matroid-based algorithm for submodular welfare problem (2015)
  17. Bonato, Thorsten; Jünger, Michael; Reinelt, Gerhard; Rinaldi, Giovanni: Lifting and separation procedures for the cut polytope (2014)
  18. Jiao, Hong-Wei; Huang, Ya-Kui; Chen, Jing: A novel approach for solving semidefinite programs (2014)
  19. Krislock, Nathan; Malick, Jérôme; Roupin, Frédéric: Improved semidefinite bounding procedure for solving max-cut problems to optimality (2014)
  20. Letchford, Adam N.; Sørensen, Michael M.: A new separation algorithm for the Boolean quadric and cut polytopes (2014)

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