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 267 articles , 1 standard article )

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  1. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  2. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  3. Berthold, Timo; Hendel, Gregor; Koch, Thorsten: From feasibility to improvement to proof: three phases of solving mixed-integer programs (2018)
  4. Berthold, Timo; Perregaard, Michael; Mészáros, Csaba: Four good reasons to use an interior point solver within a MIP solver (2018)
  5. Chen, Wei-Kun; Chen, Liang; Yang, Mu-Ming; Dai, Yu-Hong: Generalized coefficient strengthening cuts for mixed integer programming (2018)
  6. da Costa Vieira Rezende, Josiane; Souza, Marcone Jamilson Freitas; Coelho, Vitor Nazário; Martins, Alexandre Xavier: HMS: a hybrid multi-start algorithm for solving binary linear programs (2018)
  7. Delorme, Maxence; Iori, Manuel; Martello, Silvano: BPPLIB: a library for bin packing and cutting stock problems (2018)
  8. Dey, Santanu S.; Iroume, Andres; Molinaro, Marco; Salvagnin, Domenico: Improving the randomization step in feasibility pump (2018)
  9. Dey, Santanu S.; Molinaro, Marco: Theoretical challenges towards cutting-plane selection (2018)
  10. Fischetti, Matteo; Ljubić, Ivana; Monaci, Michele; Sinnl, Markus: On the use of intersection cuts for bilevel optimization (2018)
  11. Fischetti, Matteo; Monaci, Michele; Salvagnin, Domenico: SelfSplit parallelization for mixed-integer linear programming (2018)
  12. Gamrath, Gerald; Schubert, Christoph: Measuring the impact of branching rules for mixed-integer programming (2018)
  13. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  14. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  15. Ladisch, Frieder; Schürmann, Achill: Equivalence of lattice orbit polytopes (2018)
  16. Le Bodic, Pierre; Pavelka, Jeffrey W.; Pfetsch, Marc E.; Pokutta, Sebastian: Solving MIPs via scaling-based augmentation (2018)
  17. Munguía, Lluís-Miquel; Ahmed, Shabbir; Bader, David A.; Nemhauser, George L.; Shao, Yufen: Alternating criteria search: a parallel large neighborhood search algorithm for mixed integer programs (2018)
  18. Rihm, Tom; Baumann, Philipp: Staff assignment with lexicographically ordered acceptance levels (2018)
  19. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: ParaXpress: an experimental extension of the FICO Xpress-Optimizer to solve hard MIPs on supercomputers (2018)
  20. Vigerske, Stefan; Gleixner, Ambros: SCIP: global optimization of mixed-integer nonlinear programs in a branch-and-cut framework (2018)

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