A mixed integer (linear) program (mip) is an optimization problem in which a linear objective function is minimized subject to linear constraints over real- and integervalued variables. For details on mixed integer programming, see, e.g., [69,106]. The miplib is a diverse collection of challenging real-world mip instances from various academic and industrial applications suited for benchmarking and testing of mip solution algorithms.

References in zbMATH (referenced in 204 articles , 1 standard article )

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  1. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2016)
  2. Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
  3. Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
  4. Salvagnin, Domenico: Detecting semantic groups in MIP models (2016)
  5. Santos, Haroldo G.; Toffolo, Túlio A.M.; Gomes, Rafael A.M.; Ribas, Sabir: Integer programming techniques for the nurse rostering problem (2016)
  6. Yuh, Junsang; Lee, Youngho: Surrogate-RLT cuts for zero-one integer programs (2016)
  7. Bergner, Martin; Caprara, Alberto; Ceselli, Alberto; Furini, Fabio; Lübbecke, Marco E.; Malaguti, Enrico; Traversi, Emiliano: Automatic Dantzig-Wolfe reformulation of mixed integer programs (2015)
  8. Bonami, Pierre; Lodi, Andrea; Tramontani, Andrea; Wiese, Sven: On mathematical programming with indicator constraints (2015)
  9. Bonami, Pierre; Margot, François: Cut generation through binarization (2015)
  10. Caramia, Massimiliano; Mari, Renato: Enhanced exact algorithms for discrete bilevel linear problems (2015)
  11. Gamrath, Gerald; Koch, Thorsten; Martin, Alexander; Miltenberger, Matthias; Weninger, Dieter: Progress in presolving for mixed integer programming (2015)
  12. Gleixner, Ambros M.: Exact and fast algorithms for mixed-integer nonlinear programming (2015)
  13. Hendel, Gregor: Enhancing MIP branching decisions by using the sample variance of pseudo costs (2015)
  14. Honarmand, Masoud; Zakariazadeh, Alireza; Jadid, Shahram: Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization (2015)
  15. Louveaux, Quentin; Poirrier, Laurent; Salvagnin, Domenico: The strength of multi-row models (2015)
  16. Paparrizos, Konstantinos; Samaras, Nikolaos; Sifaleras, Angelo: Exterior point simplex-type algorithms for linear and network optimization problems (2015)
  17. Xie, Lin; Suhl, Leena: Cyclic and non-cyclic crew rostering problems in public bus transit (2015)
  18. Berthold, Timo: RENS. The optimal rounding (2014)
  19. Boland, Natashia; Eberhard, Andrew; Engineer, Faramroze; Fischetti, Matteo; Savelsbergh, Martin; Tsoukalas, Angelos: Boosting the feasibility pump (2014)
  20. Bussieck, Michael R.; Dirkse, Steven P.; Vigerske, Stefan: PAVER 2.0: an open source environment for automated performance analysis of benchmarking data (2014)

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Further publications can be found at: http://miplib.zib.de/biblio.html