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 340 articles , 4 standard articles )

Showing results 21 to 40 of 340.
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  1. Gamrath, Gerald; Schubert, Christoph: Measuring the impact of branching rules for mixed-integer programming (2018)
  2. Gomes, Ricardo J.; Guerreiro, Andreia P.; Kuhn, Tobias; Paquete, Luís: Implicit enumeration strategies for the hypervolume subset selection problem (2018)
  3. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  4. Hiller, Benjamin; Koch, Thorsten; Schewe, Lars; Schwarz, Robert; Schweiger, Jonas: A system to evaluate gas network capacities: concepts and implementation (2018)
  5. Hooker, J. N.; van Hoeve, W.-J.: Constraint programming and operations research (2018)
  6. Hoppmann, Kai; Schwarz, Robert: Finding maximum minimum cost flows to evaluate gas network capacities (2018)
  7. Khajavirad, Aida; Sahinidis, Nikolaos V.: A hybrid LP/NLP paradigm for global optimization relaxations (2018)
  8. Kılınç, Mustafa R.; Sahinidis, Nikolaos V.: Exploiting integrality in the global optimization of mixed-integer nonlinear programming problems with BARON (2018)
  9. Kim, Kibaek; Zavala, Victor M.: Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs (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. Lara, Cristiana L.; Trespalacios, Francisco; Grossmann, Ignacio E.: Global optimization algorithm for capacitated multi-facility continuous location-allocation problems (2018)
  13. Le Bodic, Pierre; Pavelka, Jeffrey W.; Pfetsch, Marc E.; Pokutta, Sebastian: Solving MIPs via scaling-based augmentation (2018)
  14. Lee, Jon; Skipper, Daphne; Speakman, Emily: Algorithmic and modeling insights via volumetric comparison of polyhedral relaxations (2018)
  15. Leise, Philipp; Altherr, Lena C.; Pelz, Peter F.: Energy-efficient design of a water supply system for skyscrapers by mixed-integer nonlinear programming (2018)
  16. Lim, Cong Han; Linderoth, Jeff; Luedtke, James: Valid inequalities for separable concave constraints with indicator variables (2018)
  17. Lodi, Andrea; Moradi, Ahmad: Experiments on virtual private network design with concave capacity costs (2018)
  18. López, C. O.; Beasley, J. E.: Packing unequal rectangles and squares in a fixed size circular container using formulation space search (2018)
  19. Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Polyhedral approximation in mixed-integer convex optimization (2018)
  20. Malone, Brandon; Kangas, Kustaa; Järvisalo, Matti; Koivisto, Mikko; Myllymäki, Petri: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction (2018)

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