Advanced Optimization for Process Systems Engineering

This chapter addresses the decomposition of MILP optimization problems that involve complicating constraints. It is shown that, by dualizing the complicating constraints, one can derive the Lagrangean relaxation that yields a lower bound to the optimal solution. It is also shown that, by duplicating variables, one can dualize the corresponding equalities yielding the Lagrangean decomposition method that can predict stronger lower bounds than the Lagrangean relaxationn. The steps involved in this decomposition method are described, and can be exended to NLP and MINLP problems.