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 260 articles , 4 standard articles )

Showing results 1 to 20 of 260.
Sorted by year (citations)

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  1. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  2. Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
  3. Bartlett, Mark; Cussens, James: Integer linear programming for the Bayesian network structure learning problem (2017)
  4. Beck, Amir; Pan, Dror: A branch and bound algorithm for nonconvex quadratic optimization with ball and linear constraints (2017)
  5. Belotti, Pietro; Berthold, Timo: Three ideas for a feasibility pump for nonconvex MINLP (2017)
  6. Berg, Jeremias; Järvisalo, Matti: Cost-optimal constrained correlation clustering via weighted partial maximum satisfiability (2017)
  7. Berthold, Timo: Improving the performance of MIP and MINLP solvers by integrated heuristics (2017)
  8. Brinkmann, Philip; Ziegler, Günter M.: A flag vector of a 3-sphere that is not the flag vector of a 4-polytope (2017)
  9. Cheung, Kevin K.H.; Gleixner, Ambros; Steffy, Daniel E.: Verifying integer programming results (2017)
  10. Cussens, James; Järvisalo, Matti; Korhonen, Janne H.; Bartlett, Mark: Bayesian network structure learning with integer programming: polytopes, facets and complexity (2017)
  11. Dilkina, Bistra; Khalil, Elias B.; Nemhauser, George L.: Comments on: “On learning and branching: a survey” (2017)
  12. Firsching, Moritz: Realizability and inscribability for simplicial polytopes via nonlinear optimization (2017)
  13. Gerard, D.; Köppe, M.; Louveaux, Q.: Guided dive for the spatial branch-and-bound (2017)
  14. Ghasemi, Mohammad S.; Afzalian, Ali A.: Robust tube-based MPC of constrained piecewise affine systems with bounded additive disturbances (2017)
  15. Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
  16. Göttlich, Simone; Potschka, Andreas; Ziegler, Ute: Partial outer convexification for traffic light optimization in road networks (2017)
  17. Guns, Tias; Dries, Anton; Nijssen, Siegfried; Tack, Guido; De Raedt, Luc: MiningZinc: A declarative framework for constraint-based mining (2017)
  18. Hanafi, Saïd; Todosijević, Raca: Mathematical programming based heuristics for the 0--1 MIP: a survey (2017)
  19. Haws, David; Cussens, James; Studený, Milan: Polyhedral approaches to learning Bayesian networks (2017)
  20. Hendel, Gregor: Exploiting solving phases for mixed-integer programs (2017)

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