|Subject:||[Help-glpk] Many basic vars = 0, many non-basic are on upper-bound|
|Date:||Thu, 11 Jun 2009 19:26:05 -0700|
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.
Hotmail® has ever-growing storage! Don’t worry about storage limits. Check it out.
|[Prev in Thread]||Current Thread||[Next in Thread]|