A subgradient-based branch-and-bound algorithm for the ...

dual bound resulting from relaxing demand constraints in the CFLP. (ii) This Lagrangean
dual bound is generally a sharp lower bound on the optimal objective
function value of the CFLP. (iii) The number of binary variables is only a small percentage
of the total number of variables. The good results obtained for the CFLP
with this subgradient-based approach and the simple averaging of Lagrangean solutions
for primal solution recovery suggests that similar results may possibly also be
obtainable for mixed-integer programming problems, which only show a relatively
small number of integer variables and where subgradient optimization works well in
providing sharp lower bounds.
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