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 295 articles , 4 standard articles )

Showing results 1 to 20 of 295.
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  1. Altherr, Lena C.; Dörig, Bastian; Ederer, Thorsten; Pelz, Peter F.; Pfetsch, Marc E.; Wolf, Jan: A mixed-integer nonlinear program for the design of gearboxes (2018)
  2. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  3. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  4. Berthold, Timo; Hendel, Gregor; Koch, Thorsten: From feasibility to improvement to proof: three phases of solving mixed-integer programs (2018)
  5. Bus, Norbert; Mustafa, Nabil H.; Ray, Saurabh: Practical and efficient algorithms for the geometric hitting set problem (2018)
  6. Chen, Wei-Kun; Chen, Liang; Yang, Mu-Ming; Dai, Yu-Hong: Generalized coefficient strengthening cuts for mixed integer programming (2018)
  7. Delorme, Maxence; Iori, Manuel; Martello, Silvano: BPPLIB: a library for bin packing and cutting stock problems (2018)
  8. Gally, Tristan; Pfetsch, Marc E.; Ulbrich, Stefan: A framework for solving mixed-integer semidefinite programs (2018)
  9. Kılınç, Mustafa R.; Sahinidis, Nikolaos V.: Exploiting integrality in the global optimization of mixed-integer nonlinear programming problems with BARON (2018)
  10. Kimura, Keiji; Waki, Hayato: Minimization of Akaike’s information criterion in linear regression analysis via mixed integer nonlinear program (2018)
  11. Lancia, Giuseppe; Serafini, Paolo: Compact extended linear programming models (2018)
  12. Malone, Brandon; Kangas, Kustaa; Järvisalo, Matti; Koivisto, Mikko; Myllymäki, Petri: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction (2018)
  13. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: ParaXpress: an experimental extension of the FICO Xpress-Optimizer to solve hard MIPs on supercomputers (2018)
  14. Thammawichai, Mason; Kerrigan, Eric C.: Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors (2018)
  15. Vigerske, Stefan; Gleixner, Ambros: SCIP: global optimization of mixed-integer nonlinear programs in a branch-and-cut framework (2018)
  16. Witzig, Jakob; Beckenbach, Isabel; Eifler, Leon; Fackeldey, Konstantin; Gleixner, Ambros; Grever, Andreas; Weber, Marcus: Mixed-integer programming for cycle detection in nonreversible Markov processes (2018)
  17. 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)
  18. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  19. Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
  20. Bartlett, Mark; Cussens, James: Integer linear programming for the Bayesian network structure learning problem (2017)

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