Output from glpsol: The information includes: - the current number of iterations performed by the simplex solver; - the objective value for the best known integer feasible solution, which is upper (minimization) or lower (maximization) global bound for optimal solution of the original mip problem; - the best local bound for active nodes, which is lower (minimization) or upper (maximization) global bound for optimal solution of the original mip problem; - the relative mip gap, in percents; - the number of open (active) subproblems; - the number of completely explored subproblems, i.e. whose nodes have been already removed from the tree. An example from a MIP problem: Solving LP relaxation... 0: objval = 1.552850000e+07 infeas = 1.000000000e+00 (0) 37: objval = 1.270460000e+07 infeas = 0.000000000e+00 (0) * 37: objval = 1.270460000e+07 infeas = 0.000000000e+00 (0) * 104: objval = 9.071832857e+06 infeas = 0.000000000e+00 (0) OPTIMAL SOLUTION FOUND Integer optimization begins... + 104: mip = not found yet >= -inf (1; 0) + 114: mip = 9.122800000e+06 >= 9.071832857e+06 0.6% (6; 0) + 128: mip = 9.114940000e+06 >= 9.071832857e+06 0.5% (8; 1) + 131: mip = 9.105080000e+06 >= 9.071832857e+06 0.4% (8; 2) + 140: mip = 9.105080000e+06 >= tree is empty 0.0% (0; 21) INTEGER OPTIMAL SOLUTION FOUND It takes 104 simplex iterations to solve the first LP-problem. The optimal objective function value of the LP-relaxation is 9.071832857e+06. It takes a total of 140 simplex iterations to solve the MIP. The optimal objective function value is 9.105080000e+06. After the optimal integer solution is found, 21 branch-and-bound nodes have been investigated.