From Cornell University Computational Optimization Open Textbook - Optimization Wiki
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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> |
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| ==Conclusion== | | ==Conclusion== |
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| ==References== | | ==References== |
Revision as of 06:46, 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