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 223 articles , 1 standard article )

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  1. Alvarez, Alejandro Marcos; Louveaux, Quentin; Wehenkel, Louis: A machine learning-based approximation of strong branching (2017)
  2. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  3. Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
  4. Belotti, Pietro; Bonami, Pierre; Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Nogales-Gómez, Amaya; Salvagnin, Domenico: On handling indicator constraints in mixed integer programming (2016)
  5. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2016)
  6. Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
  7. Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
  8. Jakob Witzig, Timo Berthold, Stefan Heinz: Experiments with Conflict Analysis in Mixed Integer Programming (2016) arXiv
  9. Kevin K. H. Cheung, Ambros Gleixner, Daniel E. Steffy: Verifying Integer Programming Results (2016) arXiv
  10. Ku, Wen-Yang; Beck, J.Christopher: Mixed integer programming models for job shop scheduling: A computational analysis (2016)
  11. Lalla-Ruiz, Eduardo; Voß, Stefan: Modeling the parallel machine scheduling problem with step deteriorating jobs (2016)
  12. Liberto, Giovanni Di; Kadioglu, Serdar; Leo, Kevin; Malitsky, Yuri: DASH: dynamic approach for switching heuristics (2016)
  13. Rebennack, Steffen: Computing tight bounds via piecewise linear functions through the example of circle cutting problems (2016)
  14. Salvagnin, Domenico: Detecting semantic groups in MIP models (2016)
  15. Santos, Haroldo G.; Toffolo, Túlio A.M.; Gomes, Rafael A.M.; Ribas, Sabir: Integer programming techniques for the nurse rostering problem (2016)
  16. Yuh, Junsang; Lee, Youngho: Surrogate-RLT cuts for zero-one integer programs (2016)
  17. Baena, Daniel; Castro, Jordi; González, José A.: Fix-and-relax approaches for controlled tabular adjustment (2015)
  18. Bergner, Martin; Caprara, Alberto; Ceselli, Alberto; Furini, Fabio; Lübbecke, Marco E.; Malaguti, Enrico; Traversi, Emiliano: Automatic Dantzig-Wolfe reformulation of mixed integer programs (2015)
  19. Berthold, Timo; Hendel, Gregor: Shift-and-propagate (2015)
  20. Bonami, Pierre; Lodi, Andrea; Tramontani, Andrea; Wiese, Sven: On mathematical programming with indicator constraints (2015)

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