From Cornell University Computational Optimization Open Textbook - Optimization Wiki
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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> |
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| | === Example 2 === |
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| ==Conclusion== | | ==Conclusion== |
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| ==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