Outer-approximation (OA): Difference between revisions

From Cornell University Computational Optimization Open Textbook - Optimization Wiki
Jump to navigation Jump to search
Line 51: Line 51:
<math display=block>0 \leq x_{2}\leq 4</math>
<math display=block>0 \leq x_{2}\leq 4</math>
<math display=block>y_{1},y_{2} \in  \big\{0,1\big\} </math>
<math display=block>y_{1},y_{2} \in  \big\{0,1\big\} </math>
''MILP Solution: ''<math display=inline>x_{1}=2, x_{2}=1,y_{1}=1, y_{2}=0</math>, Lower Bound = 6 <br>
Lower Bound<Upper Bound, Integer Cut:<math display=inline>y_{1}- y_{2}\leq 0</math>


==Conclusion==
==Conclusion==


==References==
==References==

Revision as of 07:32, 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 1a: Solve the MILP master problem with OA for  :


Minimize

Subject to

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

Conclusion

References