Outer-approximation (OA): Difference between revisions

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== Example ==
== Example ==
=== Example 1 ===
''Minimize'' <math display=block> f(x)= y_{1} +y_{2} + \big(x_{1}\big)^{2} +\big(x_{2}\big)^{2} </math>
''Minimize'' <math display=block> f(x)= y_{1} +y_{2} + \big(x_{1}\big)^{2} +\big(x_{2}\big)^{2} </math>
''Subject to'' <math display=block>\big(x_{1}-2\big)^{2}-x_{2} \leq 0</math>
''Subject to'' <math display=block>\big(x_{1}-2\big)^{2}-x_{2} \leq 0</math>
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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><br>
''Optimal Solution for the MINLP: ''<math display=inline>x_{1}=2, x_{2}=1,y_{1}=1, y_{2}=0</math><br>
=== Example 2 ===


==Conclusion==
==Conclusion==


==References==
==References==

Revision as of 06:48, 26 November 2021

Author: Yousef Aloufi (CHEME 6800 Fall 2021)

Introduction

Theory

Example

Example 1

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:

Example 2

Conclusion

References