> I have the following problem: I have a parameter A with (3 indices) > and a decision variable X also with 3 indices (same matrix form). > The idea is as follows: Parameter A is the old matrix, X is the new > matrix. Now I need to be sure that at most 10 changes are made. So I > would like to have at most 10 different X[a,b,c] to A[a,b,c] for all > a,b,c.
> Now I was used to programs that could work with absolute values > (SUM[a,b,c]: abs(A[a,b,c]-X[a,b,c]) <= 10), however GLPK is not able > to work with absolute values of decision variables. > Does anyone have an idea how to solve this? > I tried introducing a Dummy: D[a,b,c] = 1 for A[a,b,c]<=X[a,b,c], 0 > else but that didn't work out.... Big M formulation: "if z1 then x <= a - eps else x <= +M" can be modeled as x <= (a - eps) * z1 + M * (1 - z1) "if z2 then x >= a + eps else x >= -M" can be modeled as x >= (a + eps) * z2 - M * (1 - z2) "if z then x <= a - eps or x >= a + eps else a - eps < x < a + eps" is equivalent to "z = z1 or z2" and can be modeled as 0 <= 2 * z - z1 - z2 <= 1 where z1, z2, z are binary variables. _______________________________________________ Help-glpk mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-glpk
