MIPLIB

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 235 articles , 1 standard article )

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  1. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  2. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  3. Alvarez, Alejandro Marcos; Louveaux, Quentin; Wehenkel, Louis: A machine learning-based approximation of strong branching (2017)
  4. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  5. Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
  6. Berthold, Timo: Improving the performance of MIP and MINLP solvers by integrated heuristics (2017)
  7. Cheung, Kevin K.H.; Gleixner, Ambros; Steffy, Daniel E.: Verifying integer programming results (2017)
  8. Geißler, Björn; Morsi, Antonio; Schewe, Lars; Schmidt, Martin: Penalty alternating direction methods for mixed-integer optimization: a new view on feasibility pumps (2017)
  9. Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
  10. Kruber, Markus; Lübbecke, Marco E.; Parmentier, Axel: Learning when to use a decomposition (2017)
  11. Lodi, Andrea; Zarpellon, Giulia: On learning and branching: a survey (2017)
  12. Souza Brito, Samuel; Santos, Haroldo Gambini: Automatic integer programming reformulation using variable neighborhood search (2017)
  13. Witzig, Jakob; Berthold, Timo; Heinz, Stefan: Experiments with conflict analysis in mixed integer programming (2017)
  14. Belotti, Pietro; Bonami, Pierre; Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Nogales-Gómez, Amaya; Salvagnin, Domenico: On handling indicator constraints in mixed integer programming (2016)
  15. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2016)
  16. Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
  17. Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
  18. Jakob Witzig, Timo Berthold, Stefan Heinz: Experiments with Conflict Analysis in Mixed Integer Programming (2016) arXiv
  19. Kevin K. H. Cheung, Ambros Gleixner, Daniel E. Steffy: Verifying Integer Programming Results (2016) arXiv
  20. Ku, Wen-Yang; Beck, J.Christopher: Mixed integer programming models for job shop scheduling: A computational analysis (2016)

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