Thanks for the tip. I did try the exact solver but it is very-very slow, as i expected.
My LP problems are generated by successive linearization of a nonlinear problem, and i need to automate the solution process. So my problem is not only for this particular LP problem, i need an error estimate on the objective function value for each solved LP problem. I checked the KKT conditions and all the solution and feasibility (both for primal and dual) qualities turned out to be high without exception during the entire solution process. So my question boils down to the following: how accurate is "quite accurate"? Which parameter(s) determines the accuracy of the objective function value? Please note that my objective has at most one variable in it with coefficient 1.0 (min / max x_j or zero objective) and the problem is scaled with lpx_scale_prob, the KKT properties are checked for the internally scaled problem. Ali _______________________________________________ Help-glpk mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-glpk
