From Cornell University Computational Optimization Open Textbook - Optimization Wiki
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| == Example == | | == Example == |
| === Example 1 === | | === Numerical Example === |
| ''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> |
Revision as of 07:40, 26 November 2021
Author: Yousef Aloufi (CHEME 6800 Fall 2021)
Introduction
Theory
Example
Numerical 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:
Example 2
Conclusion
References