TY - GEN A1 - Cook, William A1 - Koch, Thorsten A1 - Steffy, Daniel A1 - Wolter, Kati T1 - A Hybrid Branch-and-Bound Approach for Exact Rational Mixed-Integer Programming N2 - We present an exact rational solver for mixed-integer linear programming that avoids the numerical inaccuracies inherent in the floating-point computations used by existing software. This allows the solver to be used for establishing theoretical results and in applications where correct solutions are critical due to legal and financial consequences. Our solver is a hybrid symbolic/numeric implementation of LP-based branch-and-bound, using numerically-safe methods for all binding computations in the search tree. Computing provably accurate solutions by dynamically choosing the fastest of several safe dual bounding methods depending on the structure of the instance, our exact solver is only moderately slower than an inexact floating-point branch-and-bound solver. The software is incorporated into the SCIP optimization framework, using the exact LP solver QSopt_ex and the GMP arithmetic library. Computational results are presented for a suite of test instances taken from the MIPLIB and Mittelmann libraries and for a new collection of numerically difficult instances. T3 - ZIB-Report - 12-49 Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-17171 SN - 1438-0064 ER - TY - CHAP A1 - Gleixner, Ambros A1 - Steffy, Daniel A1 - Wolter, Kati T1 - Improving the Accuracy of Linear Programming Solvers with Iterative Refinement T2 - ISSAC '12. Proceedings of the 37th International Symposium on Symbolic and Algebraic Computation Y1 - 2012 U6 - https://doi.org/10.1145/2442829.2442858 SP - 187 EP - 194 ER - TY - GEN A1 - Gleixner, Ambros A1 - Steffy, Daniel A1 - Wolter, Kati T1 - Improving the Accuracy of Linear Programming Solvers with Iterative Refinement N2 - We describe an iterative refinement procedure for computing extended precision or exact solutions to linear programming problems (LPs). Arbitrarily precise solutions can be computed by solving a sequence of closely related LPs with limited precision arithmetic. The LPs solved share the same constraint matrix as the original problem instance and are transformed only by modification of the objective function, right-hand side, and variable bounds. Exact computation is used to compute and store the exact representation of the transformed problems, while numeric computation is used for solving LPs. At all steps of the algorithm the LP bases encountered in the transformed problems correspond directly to LP bases in the original problem description. We demonstrate that this algorithm is effective in practice for computing extended precision solutions and that this leads to direct improvement of the best known methods for solving LPs exactly over the rational numbers. T3 - ZIB-Report - 12-19 KW - Linear programming KW - Iterative refinement KW - Exact linear programming Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-15451 SN - 1438-0064 ER -