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 542 articles , 4 standard articles )

Showing results 61 to 80 of 542.
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  1. Sharma, Meenarli; Hahn, Mirko; Leyffer, Sven; Ruthotto, Lars; van Bloemen Waanders, Bart: Inversion of convection-diffusion equation with discrete sources (2021)
  2. Sundar, Kaarthik; Nagarajan, Harsha; Linderoth, Jeff; Wang, Site; Bent, Russell: Piecewise polyhedral formulations for a multilinear term (2021)
  3. Tjandraatmadja, Christian; van Hoeve, Willem-Jan: Incorporating bounds from decision diagrams into integer programming (2021)
  4. Vasallo, Manuel Jesús; Cojocaru, Emilian Gelu; Gegúndez, Manuel Emilio; Marín, Diego: Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants (2021)
  5. Wang, Akang; Gounaris, Chrysanthos E.: On tackling reverse convex constraints for non-overlapping of unequal circles (2021)
  6. Witzig, Jakob; Berthold, Timo; Heinz, Stefan: Computational aspects of infeasibility analysis in mixed integer programming (2021)
  7. Witzig, Jakob; Gleixner, Ambros: Conflict-driven heuristics for mixed integer programming (2021)
  8. Wolsey, Laurence A.: Integer programming (2021)
  9. Achterberg, Tobias; Bixby, Robert E.; Gu, Zonghao; Rothberg, Edward; Weninger, Dieter: Presolve reductions in mixed integer programming (2020)
  10. Aleksander Figiel, Anne-Sophie Himmel, Andre Nichterlein, Rolf Niedermeier: On 2-Clubs in Graph-Based Data Clustering: Theory and Algorithm Engineering (2020) arXiv
  11. Alimo, Ryan; Beyhaghi, Pooriya; Bewley, Thomas R.: Delaunay-based derivative-free optimization via global surrogates. III: nonconvex constraints (2020)
  12. Basso, S.; Ceselli, Alberto; Tettamanzi, Andrea: Random sampling and machine learning to understand good decompositions (2020)
  13. Benadè, Gerdus; Hooker, John N.: Optimization bounds from the branching dual (2020)
  14. Bogdanov, I. P.; Sudakov, V. A.; Toporov, N. B.: Loading optimization of an ordered set of aircrafts (2020)
  15. Bulhões, Teobaldo; Sadykov, Ruslan; Subramanian, Anand; Uchoa, Eduardo: On the exact solution of a large class of parallel machine scheduling problems (2020)
  16. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  17. Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
  18. Deleplanque, Samuel; Labbé, Martine; Ponce, Diego; Puerto, Justo: A branch-price-and-cut procedure for the discrete ordered Median problem (2020)
  19. Del Pia, Alberto; Khajavirad, Aida; Sahinidis, Nikolaos V.: On the impact of running intersection inequalities for globally solving polynomial optimization problems (2020)
  20. De Santis, Marianna; Eichfelder, Gabriele; Niebling, Julia; Rocktäschel, Stefan: Solving multiobjective mixed integer convex optimization problems (2020)

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