> That you need is another kind of post-optimal analysis, so > called > parametric analysis. You define the objective function or > rhs vector > to depend on some scalar parameter t, for example: > > z = (c[1] + t*c'[1])*x[1] + ... + (c[n] + > t*c'[n])*x[n], > > and then see how the optimal solution is changing on > varying t. > > There exist efficient methods to perform the parametric > analysis, > unfortunately, in glpk this feature is not implemented yet. > (I have it > in the to-do list.)
I'm looking forward to it. In the example you have given, the params are linearly dependent on t -- what if the dependence is non-linear? > > > One possible way I am thinking is to do something like > a binary > > search, and test if the basis remains optimal a long > the way. > > This technique may work, however, it is inefficient (and > non-elegant). > When the params (not just rhs, coefs, but also any entry in the contraint matrix) depends nonlinearly on scaler t, I wonder if there are some efficient algorithms. This problem seems extremely hard, as the set of t that shares the same optimal basis might be many disjoint intervals. We might just be satisfied with identifying one particular interval of t that contains some initial t_0. _______________________________________________ Help-glpk mailing list Help-glpk@gnu.org http://lists.gnu.org/mailman/listinfo/help-glpk