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 228 articles , 4 standard articles )

Showing results 21 to 40 of 228.
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  1. Braun, Gábor; Pokutta, Sebastian: A polyhedral characterization of border bases (2016)
  2. Bruns, Winfried; Sieg, Richard; Söger, Christof: The subdivision of large simplicial cones in normaliz (2016)
  3. Buchheim, Christoph; De Santis, Marianna; Lucidi, Stefano; Rinaldi, Francesco; Trieu, Long: A feasible active set method with reoptimization for convex quadratic mixed-integer programming (2016)
  4. Csercsik, Dávid: Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade (2016)
  5. D’Andreagiovanni, Fabio; Gleixner, Ambros M.: Towards an accurate solution of wireless network design problems (2016)
  6. Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
  7. Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
  8. Greuel, Gert-Martin (ed.); Koch, Thorsten (ed.); Paule, Peter (ed.); Sommese, Andrew (ed.): Mathematical software -- ICMS 2016. 5th international conference, Berlin, Germany, July 11--14, 2016. Proceedings (2016)
  9. Grimm, Veronika; Martin, Alexander; Schmidt, Martin; Weibelzahl, Martin; Zöttl, Gregor: Transmission and generation investment in electricity markets: the effects of market splitting and network fee regimes (2016)
  10. Hojny, Christopher; Pfetsch, Marc E.: A polyhedral investigation of star colorings (2016)
  11. Jakob Witzig, Timo Berthold, Stefan Heinz: Experiments with Conflict Analysis in Mixed Integer Programming (2016) arXiv
  12. Johnston, Matthew D.: A linear programming approach to dynamical equivalence, linear conjugacy, and the deficiency one theorem (2016)
  13. Johnston, Matthew D.; Pantea, Casian; Donnell, Pete: A computational approach to persistence, permanence, and endotacticity of biochemical reaction systems (2016)
  14. Keiji Kimura, Hayato Waki: Minimization of Akaike’s Information Criterion in Linear Regression Analysis via Mixed Integer Nonlinear Program (2016) arXiv
  15. Kevin K. H. Cheung, Ambros Gleixner, Daniel E. Steffy: Verifying Integer Programming Results (2016) arXiv
  16. Kimura, Keiji; Waki, Hayato: Mixed integer nonlinear program for minimization of Akaike’s information criterion (2016)
  17. Ku, Wen-Yang; Beck, J.Christopher: Mixed integer programming models for job shop scheduling: A computational analysis (2016)
  18. López, C.O.; Beasley, J.E.: A formulation space search heuristic for packing unequal circles in a fixed size circular container (2016)
  19. Maher, Stephen; Miltenberger, Matthias; Pedroso, João Pedro; Rehfeldt, Daniel; Schwarz, Robert; Serrano, Felipe: PySCIPOpt: mathematical programming in python with the SCIP optimization suite (2016)
  20. Miles Lubin, Emre Yamangil, Russell Bent, Juan Pablo Vielma: Polyhedral approximation in mixed-integer convex optimization (2016) arXiv

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