Outer-approximation (OA): Difference between revisions

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''Solution: ''<math display=inline>x_{1}=2, x_{2}=1</math>, Upper Bound = 6 <br>
''Solution: ''<math display=inline>x_{1}=2, x_{2}=1</math>, Upper Bound = 6 <br>
Upper Bound = 6 = Lower Bound, Optimum!<br>
Upper Bound = 6 = Lower Bound, Optimum!<br>
''Optimal Solution for the MINLP: ''<math display=inline>x_{1}=2, x_{2}=1,y_{1}=1, y_{2}=0</math>, Upper Bound = 6 <br>
''Optimal Solution for the MINLP: ''<math display=inline>x_{1}=2, x_{2}=1,y_{1}=1, y_{2}=0</math><br>


==Conclusion==
==Conclusion==


==References==
==References==

Revision as of 07:46, 26 November 2021

Author: Yousef Aloufi (CHEME 6800 Fall 2021)

Introduction

Theory

Example

Minimize

Subject to
Solution
Step 1a: Start from and solve the NLP below:
Minimize
Subject to
Solution: , Upper Bound = 7

Step 1b: Solve the MILP master problem with OA for  :


Minimize

Subject to

MILP Solution: , Lower Bound = 6
Lower Bound < Upper Bound, Integer cut:

Step 2a: Start from and solve the NLP below:
Minimize

Subject to
Solution: , Upper Bound = 6
Upper Bound = 6 = Lower Bound, Optimum!
Optimal Solution for the MINLP:

Conclusion

References