Invited Session Tue.1.H 1058

Tuesday, 10:30 - 12:00 h, Room: H 1058

Cluster 10: Implementations & software [...]

MILP software II


Chair: Thorsten Koch



Tuesday, 10:30 - 10:55 h, Room: H 1058, Talk 1

Martin Tieves
Creating synergies between MIP-solvers

Coauthors: Arie Koster, Manuel Kutschka


Mixed integer programming offers a broad field of interest for both, research and application. Therefore a wide range of MIP solvers is available, each with its own advantages and disadvantages. In this paper we analyze the potential of combining different solvers and/or different parameter settings in a parallel computation. Hereby, we focus on combinations of the solvers SCIP, CPLEX and GUROBI extended with different strategies of searching in the branch and bound tree. These solvers are run as blackbox-solvers on parallel threads, exchanging
potential information as primal solutions or dual bounds via callbacks at
We apply the above on the MIPLIB data-sets and show a promising speed-up
in computation for many instances.



Tuesday, 11:00 - 11:25 h, Room: H 1058, Talk 2

Michael Joswig
polymake for integer linear programming

Coauthor: Ewgenij Gawrilow


polymake is a software tool for experiments in polytope theory and related areas. More recently, new functionality useful for integer linear programming was added. This includes Hilbert bases (via interface to Normaliz), Gomory-Chvátal closures, lattice point enumeration, standard contructions, and more.
The system has been developed since 1997 and continuously expanded. Many people contributed over the years, see for the complete list.
See also a new tutorial related to optimization by Marc E. Pfetsch and Sebastian Pokutta:



Tuesday, 11:30 - 11:55 h, Room: H 1058, Talk 3

Frédéric Gardi
LocalSolver: A mathematical programming solver based on local search

Coauthors: Thierry Benoist, Julien Darlay, Bertrand Estellon, Romain Megel, Karim Nouioua


We present LocalSolver 2.0 (, a mathematical programming solver founded on local-search techniques. LocalSolver offers simple APIs as well as an efficient modeling language for fast prototyping. Actually, it is designed to tackle combinatorial problems, that is, models with 0-1 decision variables only. LocalSolver can handle very large nonlinear problems with millions of binary decisions in minutes of running times only. Its practical performance relies on innovative autonomous moves coupled with a highly-optimized incremental evaluation machinery. In this way, LocalSolver is able to explore millions of feasible solutions in minutes of running times, ensuring a fast convergence toward high-quality solutions. It has been tested on classical benchmarks and succeeded the first phase of the Google ROADEF/EURO Challenge (ranked 25th among 80 participating teams). Moreover, LocalSolver is used in several real-life applications: TV media planning, maintenance planning, energy optimization, mobile network partitioning, car sequencing, project management. For the next version, we plan to extend its capabilities to deal with mixed-variable models.


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