> My decision variables are all binary. During the solve process (for > the relaxation) many of the basic variables have a value of 0.0. This > implies degeneracy, which I feel somewhat comfortable with. At the same > time, many of my non-basic variables return GLP_NU (non-basic variable on > its upper bound, i.e. primal value is 1) when I try glp_get_col_stat(). > I'm trying to get a better understanding of what this means.
> Nothing in my model is broken and the GLPK chugs along and provides > the correct answer, so I guess I'm just asking for a little clarification > on what "non-basic on its upper bound" means in terms of the simplex > algorithm. You can find a version of the simplex method for variables with upper bounds in George Dantzig's book (Chap. 18, Variables with Upper Bounds); see http://lists.gnu.org/archive/html/help-glpk/2009-01/msg00010.html . All modern simplex-based LP solvers provide this feature. > My variables are often part of a convex combination, so the sum of > some subset of them needs to be 1. It seems odd that one of them from > this subset would be basic with a value of zero and another is non-basic > with a value of 1. I'm trying to understand what algorithmic paths might > be taken to get to such a solution. > I know the question is a tad vague, but any insight is appreciated. _______________________________________________ Help-glpk mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-glpk
