> I just want to verify my understanding about calling dual simplex and > re-optimization.
> My problem is to solve many LP of the following form: > max/min x_k > s.t. > A*x = b > l <= x <= u > for k=1,2,...,n. (x is R^n) > For each LP, only the boundary of x (i.e., l, u) are changed. > I set glpk parameter to use DUALP and presolve is OFF. > Then I call glpk to solve the above LP with different bounds. > Is this the correct step ? Yes. In this case the glpk simplex solver will start new search from the current basis, which was optimal before you changed bounds. > Is there any other calling sequence that can help reducing the total > number of simplex iterations ? Once you have solved your instance to optimality for the first time, you can save the optimal basis, i.e. row/column statuses reported by glp_get_row_stat and glp_get_col_stat. Then every time you change bounds, you can restore the initial basis using glp_set_row_stat and glp_set_col_stat before performing re-optimization. In this case glp_simplex will always start from the initial basis. _______________________________________________ Help-glpk mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-glpk
