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.