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.
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.
Thanks,
Joey
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