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 243 articles , 1 standard article )

Showing results 1 to 20 of 243.
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  1. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  2. Chen, Wei-Kun; Chen, Liang; Yang, Mu-Ming; Dai, Yu-Hong: Generalized coefficient strengthening cuts for mixed integer programming (2018)
  3. Dey, Santanu S.; Iroume, Andres; Molinaro, Marco; Salvagnin, Domenico: Improving the randomization step in feasibility pump (2018)
  4. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  5. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  6. 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)
  7. Vitor, Fabio; Easton, Todd: The double pivot simplex method (2018)
  8. Zhou, Kai; Kılınç, Mustafa R.; Chen, Xi; Sahinidis, Nikolaos V.: An efficient strategy for the activation of MIP relaxations in a multicore global MINLP solver (2018)
  9. Alvarez, Alejandro Marcos; Louveaux, Quentin; Wehenkel, Louis: A machine learning-based approximation of strong branching (2017)
  10. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  11. Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
  12. Berthold, Timo: Improving the performance of MIP and MINLP solvers by integrated heuristics (2017)
  13. Cheung, Kevin K.H.; Gleixner, Ambros; Steffy, Daniel E.: Verifying integer programming results (2017)
  14. Geißler, Björn; Morsi, Antonio; Schewe, Lars; Schmidt, Martin: Penalty alternating direction methods for mixed-integer optimization: a new view on feasibility pumps (2017)
  15. Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
  16. Guastaroba, G.; Savelsbergh, M.; Speranza, M.G.: Adaptive kernel search: a heuristic for solving mixed integer linear programs (2017)
  17. Kruber, Markus; Lübbecke, Marco E.; Parmentier, Axel: Learning when to use a decomposition (2017)
  18. Lischke, Heike; Löffler, Thomas J.: Finding all multiple stable fixpoints of $n$-species Lotka-Volterra competition models (2017)
  19. Lodi, Andrea; Zarpellon, Giulia: On learning and branching: a survey (2017)
  20. Souza Brito, Samuel; Santos, Haroldo Gambini: Automatic integer programming reformulation using variable neighborhood search (2017)

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