MINLPLib

MINLPLib -- a collection of test models for mixed-integer nonlinear programming. The paper describes a new computerized collection of test models for mixed-integer nonlinear programming. Because there is no standard format for nonlinear models, the model collection is augmented with a translation server that can transform the models from their basic GAMS format into other formats, including AMPL, BARON, LGO, LINGO, and MINOPT. The translation server can also be used to transform industrial models that contain confidential information. Such transformations allow many of these models to be distributed to the research community as highly relevant algorithmic test models.


References in zbMATH (referenced in 96 articles , 1 standard article )

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  1. Lundell, Andreas; Kronqvist, Jan: Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT (2022)
  2. Müller, Benjamin; Muñoz, Gonzalo; Gasse, Maxime; Gleixner, Ambros; Lodi, Andrea; Serrano, Felipe: On generalized surrogate duality in mixed-integer nonlinear programming (2022)
  3. Ploskas, Nikolaos; Sahinidis, Nikolaos V.: Review and comparison of algorithms and software for mixed-integer derivative-free optimization (2022)
  4. Sharma, Meenarli; Palkar, Prashant; Mahajan, Ashutosh: Linearization and parallelization schemes for convex mixed-integer nonlinear optimization (2022)
  5. Berthold, Timo; Csizmadia, Zsolt: The confined primal integral: a measure to benchmark heuristic MINLP solvers against global MINLP solvers (2021)
  6. Elloumi, Sourour; Lambert, Amélie; Lazare, Arnaud: Solving unconstrained 0-1 polynomial programs through quadratic convex reformulation (2021)
  7. Gleixner, Ambros; Hendel, Gregor; Gamrath, Gerald; Achterberg, Tobias; Bastubbe, Michael; Berthold, Timo; Christophel, Philipp; Jarck, Kati; Koch, Thorsten; Linderoth, Jeff; Lübbecke, Marco; Mittelmann, Hans D.; Ozyurt, Derya; Ralphs, Ted K.; Salvagnin, Domenico; Shinano, Yuji: MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library (2021)
  8. Kronqvist, Jan; Misener, Ruth: A disjunctive cut strengthening technique for convex MINLP (2021)
  9. Mahajan, Ashutosh; Leyffer, Sven; Linderoth, Jeff; Luedtke, James; Munson, Todd: Minotaur: a mixed-integer nonlinear optimization toolkit (2021)
  10. Muts, Pavlo; Nowak, Ivo; Hendrix, Eligius M. T.: On decomposition and multiobjective-based column and disjunctive cut generation for MINLP (2021)
  11. Neumann, Christoph; Stein, Oliver: Generating feasible points for mixed-integer convex optimization problems by inner parallel cuts (2021)
  12. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  13. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  14. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  15. De Mauri, Massimo; Gillis, Joris; Swevers, Jan; Pipeleers, Goele: A proximal-point outer approximation algorithm (2020)
  16. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  17. Furman, Kevin C.; Sawaya, Nicolas W.; Grossmann, Ignacio E.: A computationally useful algebraic representation of nonlinear disjunctive convex sets using the perspective function (2020)
  18. Kronqvist, Jan; Bernal, David E.; Grossmann, Ignacio E.: Using regularization and second order information in outer approximation for convex MINLP (2020)
  19. Müller, Benjamin; Serrano, Felipe; Gleixner, Ambros: Using two-dimensional projections for stronger separation and propagation of bilinear terms (2020)
  20. Muts, Pavlo; Nowak, Ivo; Hendrix, Eligius M. T.: The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming (2020)

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