Outer approximation is a basic approach for solving Mixed Integer Nonlinear Programming (MINLP) models suggested by Duran and Grossmann (1986) . Based on principles of decomposition, outer-approximation and relaxation, the proposed algorithm effectively exploits the structure of the original problems. The new problems consist of solving an alternating finite sequence of nonlinear programming subproblems and relaxed versions of a mixed-integer linear master program.
and should be convex.
Upper Bonding Subproblem
First, give initial values for binary variables. In the given problem, the binary variable is . Fix all the variables at and solve the new non-linear problem.
We can use the following NLP to check whether the former NLP is infeasible.
If , then former NLP is feasible. If , then infeasible.
By solving this NLP, a feasible solution is obtained. In this minimum problem, this feasible solution is greater than the optimum solution. So we can use this solution as a upper bond. Go to the master problem.
The main idea of using outer approximation is to develop equivalent linear representation of MINLP and apply relaxation. All the functions in constraints and objective should be convex and differentiable.
First reformulate the origin MINLP as follows:
Based on the solution of upper bonding problem, form a new relaxed MILP as follows:
Convergence and Optimality
To obtain a global optimum, the original MINLP should be convex, which means that all the constraints and objective function should be convex. The proposed algorithm can be applied to non-convex problems, but there is no guarantee that the solution obtained by the algorithm is a global one.
A Numerical Example
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 Fletcher R, Leyffer S. Solving mixed integer nonlinear programs by outer approximation[J]. Mathematical programming, 1994, 66(1-3): 327-349.
 Varvarezos D K, Grossmann I E, Biegler L T. An outer-approximation method for multiperiod design optimization[J]. Industrial & engineering chemistry research, 1992, 31(6): 1466-1477.
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