On March 9th, researchers from Zuse Institute Berlin, TU Darmstadt, FAU Erlangen-Nürnberg, and RWTH Aachen released the new version of the SCIP Optimization Suite 4.0.0.  SCIP is currently one of the fastest non-commercial solvers for mixed-integer programming (MIP) and mixed-integer nonlinear programming (MINLP) that can be studied in source code. The SCIP Optimization Suite provides a flexible framework for research in constraint integer programming, branch-cut-and-price, and many more extensions.

With more than 10,000 downloads during 2016, the SCIP Optimization Suite is a popular tool among optimizers all over the world. The new release comprises many new features and enhancement, such as a new concurrent solving mode, new primal heuristics, support for partial strating solutions, and new interfaces for Julia and Python. An in-depth description can be found in the technical release report

The SCIP Optimization Suite 4.0 [http://nbn-resolving.de/urn:nbn:de:0297-zib-62170]

Stephen J. Maher, Tobias Fischer, Tristan Gally, Gerald Gamrath, Ambros Gleixner, Robert Lion Gottwald, Gregor Hendel, Thorsten Koch, Marco E. Lübbecke, Matthias Miltenberger, Benjamin Müller, Marc E. Pfetsch, Christian Puchert, Daniel Rehfeldt, Sebastian Schenker, Robert Schwarz, Felipe Serrano, Yuji Shinano, Dieter Weninger, Jonas T. Witt, Jakob Witzig, ZIB-Report 17-12, Zuse Institute Berlin, March 2017

For more information see the SCIP website [http://scip.zib.de].