Yuji Shinano

TL;DR: New features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solverSCIP-SDP, and the parallelization framework UG are described.

TL;DR: The SCIP Optimization Suite 5.0 as discussed by the authors is the latest version of the constraint integer programming solver SCIP for solving mixed-integer linear and nonlinear programs, and it is about 41 % faster than SCIP 4.0 and over twice as fast on instances that take at least 100 seconds to solve.

TL;DR: For the first time, these sets were compiled using a data-driven selection process supported by the solution of a sequence of mixed integer optimization problems, which encode requirements on diversity and balancedness with respect to instance features and performance data.

TL;DR: The SCIP Optimization Suite as discussed by the authors provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP, which includes the MIP and MINLP core with new primal heuristics and a new selection criterion for cutting planes.

TL;DR: This paper highlights the new features of version 3.2 of the SCIP Optimization Suite and presents new and improved extensions of SCIP, namely solvers for multi-criteria optimization, Steiner tree problems, and mixed-integer semidefinite programs.