SCIP

SCIP is currently one of the fastest non-commercial solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP). It is also a framework for constraint integer programming and branch-cut-and-price. It allows for total control of the solution process and the access of detailed information down to the guts of the solver. SCIP is part of the SCIP Optimization Suite, which also contains the LP solver SoPlex, the modelling language ZIMPL, the parallelization framework UG and the generic column generation solver GCG.

This software is also peer reviewed by journal MPC.


References in zbMATH (referenced in 412 articles , 4 standard articles )

Showing results 1 to 20 of 412.
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  1. Basso, S.; Ceselli, Alberto; Tettamanzi, Andrea: Random sampling and machine learning to understand good decompositions (2020)
  2. Enayati, Shakiba; Özaltın, Osman Y.: Optimal influenza vaccine distribution with equity (2020)
  3. Fischer, Tobias; Pfetsch, Marc E.: On the structure of linear programs with overlapping cardinality constraints (2020)
  4. Fischetti, Matteo; Monaci, Michele: A branch-and-cut algorithm for mixed-integer bilinear programming (2020)
  5. Gleixner, Ambros; Maher, Stephen J.; Müller, Benjamin; Pedroso, João Pedro: Price-and-verify: a new algorithm for recursive circle packing using Dantzig-Wolfe decomposition (2020)
  6. Izunaga, Yoichi; Matsui, Tomomi; Yamamoto, Yoshitsugu: A doubly nonnegative relaxation for modularity density maximization (2020)
  7. Pedroso, João Pedro: Heuristics for packing semifluids (2020)
  8. Yamamura, Kiyotaka: Finding all solution sets of piecewise-linear interval equations using an integer programming solver (2020)
  9. Altherr, Lena C.; Leise, Philipp; Pfetsch, Marc E.; Schmitt, Andreas: Resilient layout, design and operation of energy-efficient water distribution networks for high-rise buildings using MINLP (2019)
  10. Bagger, Niels-Christian Fink; Desaulniers, Guy; Desrosiers, Jacques: Daily course pattern formulation and valid inequalities for the curriculum-based course timetabling problem (2019)
  11. Baumgartner, János; Süle, Zoltán; Bertók, Botond; Abonyi, János: Test-sequence optimisation by survival analysis (2019)
  12. Bei, Xiaoqiang; Zhu, Xiaoyan; Coit, David W.: A risk-averse stochastic program for integrated system design and preventive maintenance planning (2019)
  13. Bilias, Yannis; Florios, Kostas; Skouras, Spyros: Exact computation of censored least absolute deviations estimator (2019)
  14. Bojarski, Jacek; Kisielewicz, Andrzej Piotr; Przesławski, Krzysztof: Nearly neighbourly families of standard boxes (2019)
  15. Burlacu, Robert; Egger, Herbert; Groß, Martin; Martin, Alexander; Pfetsch, Marc E.; Schewe, Lars; Sirvent, Mathias; Skutella, Martin: Maximizing the storage capacity of gas networks: a global MINLP approach (2019)
  16. Cao, Yankai; Zavala, Victor M.: A scalable global optimization algorithm for stochastic nonlinear programs (2019)
  17. Casazza, Marco: New formulations for variable cost and size bin packing problems with item fragmentation (2019)
  18. Coniglio, Stefano; D’Andreagiovanni, Fabio; Furini, Fabio: A lexicographic pricer for the fractional bin packing problem (2019)
  19. Costa, Lilia; Smith, James Q.; Nichols, Thomas: A group analysis using the multiregression dynamic models for fMRI networked time series (2019)
  20. D’Ambrosio, Claudia; Frangioni, Antonio; Gentile, Claudio: Strengthening the sequential convex MINLP technique by perspective reformulations (2019)

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Further publications can be found at: http://scip.zib.de/#work