Available solvers for mixed integer programs (MIPs) focus on rapidly finding close to optimal solutions for feasible instances. The answers, however, are computed with finite precision, which might lead to wrong results. For most applications, the errors can be neglected. This situation changes fundamentally, if MIPs are used to study theoretical problems (i.e., a "computer proof" is needed), if feasibility questions are considered, and if wrong answers can have legal consequences. For such applications an exact MIP solver is required. The aim of this project is to develop such a solver.

Publications

2012
Improving the Accuracy of Linear Programming Solvers with Iterative Refinement ISSAC '12. Proceedings of the 37th International Symposium on Symbolic and Algebraic Computation, pp. 187-194, 2012 (preprint available as ZIB-Report 12-19) Ambros Gleixner, Daniel Steffy, Kati Wolter PDF (ZIB-Report)
BibTeX
DOI
Exact Integer Programming
2011
MIPLIB 2010 Mathematical Programming Computation, 3(2), pp. 103-163, 2011 (preprint available as ZIB-Report 10-31) Thorsten Koch, Tobias Achterberg, Erling Andersen, Oliver Bastert, Timo Berthold, Robert E. Bixby, Emilie Danna, Gerald Gamrath, Ambros Gleixner, Stefan Heinz, Andrea Lodi, Hans Mittelmann, Ted Ralphs, Domenico Salvagnin, Daniel Steffy, Kati Wolter PDF (ZIB-Report)
BibTeX
DOI
Exact Integer Programming